python数据分析.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# python基础知识\n",
    "## 输出格式设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我正在学习:python数据分析\n",
      "我正在家里学习python数据分析\n",
      "2018年单身人数约2.00亿\n",
      "遇见真爱的概率只有0.00002%\n"
     ]
    }
   ],
   "source": [
    "# str.format()对输出格式设置\n",
    "#一对一填充\n",
    "print('我正在学习:{}'.format('python数据分析'))\n",
    "#多对多填充\n",
    "print('我正在{}学习{}'.format('家里','python数据分析'))\n",
    "#浮点数设置  \n",
    "print('{}约{:.2f}亿'.format('2018年单身人数',2))  #  .2f 表示小数点后两位浮点型展示，.3f表示小数点后三位浮点型展示\n",
    "#百分数设置\n",
    "print('遇见真爱的概率只有{:.5%}'.format(0.0000002))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 字符串\n",
    "### 字符串的连接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'人生苦短我用python'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 直接用操作符 + 将字符串连接\n",
    "'人生苦短' + '我用python'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 字符串的复制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'python无敌python无敌python无敌'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用操作符 * 对字符串进行重复\n",
    "'python无敌' * 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取字符串长度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "print(len('我好困'))\n",
    "print(len('126483992'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 字符串查找"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n",
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# in 和 not in 查找\n",
    "print('python' in '我爱python')\n",
    "print('python' not in '我爱python')\n",
    "print('java' not in '我爱python')\n",
    "print('java' in '我爱python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "-1\n"
     ]
    }
   ],
   "source": [
    "# find 查找某一字符是否存在字符串中，存在则返回字符所在位置，不存在返回 -1\n",
    "print('abc'.find('c'))  # python 字符串位置从0开始\n",
    "print('abc'.find('B'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 字符串索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p\n",
      "h\n",
      "yth\n",
      "pyth\n",
      "on数据分析\n",
      "析\n",
      "据分析\n"
     ]
    }
   ],
   "source": [
    "a = 'python数据分析'\n",
    "print(a[0])\n",
    "print(a[3])\n",
    "print(a[1:4]) # 获取字符串中第1位到第4位的值，不包括第4位\n",
    "print(a[:4])  # 获取字符串中第0位到第4位的值，不包括第4位，第0位可以不写\n",
    "print(a[4:])  # 获取字符串中第4位到最后一位的值，最后一位可以不写\n",
    "print(a[-1])  # 获取最后一位\n",
    "print(a[-3:]) # 获取倒数第三位到最后一位，最后一位可以不写"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 字符串分隔"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'b', 'c']\n",
      "['a', 'b', 'c']\n"
     ]
    }
   ],
   "source": [
    "# 字符串用split()函数分隔，分隔后的值以列表形式返回\n",
    "# 将字符串‘a,b,c'用，分离\n",
    "print('a,b,c'.split(','))\n",
    "#将字符串‘a|b|c'用|分离\n",
    "print('a|b|c'.split('|'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 移除字符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "a\n",
      "a\n"
     ]
    }
   ],
   "source": [
    "# strip()函数用来移除字符串首尾的指定字符，默认移除字符串首尾的空格或换行符\n",
    "# 移除空格\n",
    "print('  a  '.strip())\n",
    "# 移除换行符\n",
    "print('\\ta\\t'.strip())\n",
    "# 移除指定字符\n",
    "print('AaA'.strip('A'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 列表是用来存储一组有序的数据元素的数据结构，元素之间用,分隔。\n",
    "# 列表的数据元素在[]中，[]中的数据类型可以是int、str"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 新建列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 建立空列表\n",
    "null_list = []\n",
    "# 建立 int 类型列表\n",
    "int_list = [1,2,3]\n",
    "# 建立 str 类型列表\n",
    "str_list = ['a','b','c']\n",
    "# 建立 int + str 类型列表\n",
    "int_str_list = [1,2,'a','b']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 列表的复制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 1, 2, 3]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_list *2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 列表的合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 'a', 'b', 'c']\n",
      "['a', 'b', 'c', 1, 2, 3]\n",
      "[1, 2, 3, 'a', 'b', 'c']\n"
     ]
    }
   ],
   "source": [
    "# 列表的合并有先后顺序\n",
    "# 两个列表直接用 + 连接\n",
    "print(int_list + str_list)\n",
    "print(str_list + int_list)\n",
    "# 将列表B合并到列表A，A.extend(B)。将列表A合并到列表B，B.extend(A)\n",
    "int_list.extend(str_list)\n",
    "print(int_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 列表中插入新元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4]\n",
      "['a', 'b', 'c', 't']\n",
      "[1, 2, 8, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "# append() 在列表末尾插入新数据元素，直接改变原列表，不会输出结果\n",
    "int_list = [1,2,3]\n",
    "str_list = ['a','b','c']\n",
    "int_list.append(4)\n",
    "print(int_list)\n",
    "str_list.append('t')\n",
    "print(str_list)\n",
    "# insert() 在列表的指定位置插入新数据元素，直接改变原列表，不会输出结果\n",
    "int_list.insert(2,8) #表示在第2位置插入元素8\n",
    "print(int_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取列表中值出现的次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_list = ['一班','二班','一班','二班','一班']\n",
    "score_list.count('一班')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取列表中值出现的位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "sale_list = ['锅包肉','火锅','烧烤','湘菜']\n",
    "print(sale_list.index('火锅'))\n",
    "print(score_list.index('二班')) # 获取第一个出现的元素"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取列表中指定位置的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d\n",
      "['b', 'c']\n",
      "['a', 'b', 'c', 'd']\n",
      "['d', 'e']\n"
     ]
    }
   ],
   "source": [
    "# 普通索引：获取某一特定位置的数\n",
    "v = ['a','b','c','d','e']\n",
    "print(v[3]) #获取第3位置的数\n",
    "# 切片索引：获取某一位置区间内的数\n",
    "print(v[1:3]) #获取第1位置到第3位置的数，不包括第3位置\n",
    "print(v[:4]) #获取第0位置到第4位置的数，不包括第4位置\n",
    "print(v[3:]) #获取第3位置到最后一位的数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 删除列表中的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'b', 'd']\n",
      "['a', 'b']\n"
     ]
    }
   ],
   "source": [
    "# pop() 删除指定位置的值,直接改变原列表，不输出数值\n",
    "str_list = ['a','b','c','d']\n",
    "str_list.pop(2)\n",
    "print(str_list)\n",
    "# remove() 直接删除某一元素，改变原列表，不输出数值\n",
    "str_list.remove('d')\n",
    "print(str_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对列表中的值排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8, 7, 6, 5, 4, 3, 2, 1]\n",
      "[1, 2, 3, 4, 5, 6, 7, 8]\n"
     ]
    }
   ],
   "source": [
    "# sort() reverse = True降序，reverse = False升序， 默认升序排序\n",
    "s = [1,6,3,7,2,4,8,5]\n",
    "s.sort(reverse = True)\n",
    "print(s)\n",
    "p = [1,6,3,7,2,4,8,5]\n",
    "p.sort()\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 字典"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 新建字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'张三': 18, '李四': 25}\n",
      "{'张三': 18, '李四': 25}\n",
      "{'张三': 18, '李四': 25}\n"
     ]
    }
   ],
   "source": [
    "# 先创建空字典，然后向空字典输入值\n",
    "test_dict = {}\n",
    "test_dict['张三'] = 18\n",
    "test_dict['李四'] = 25\n",
    "print(test_dict)\n",
    "# 将值直接以键值传入字典\n",
    "qaq_dict = {'张三':18,'李四':25}\n",
    "print(qaq_dict)\n",
    "# 将键值以列表形式存放在元祖中，然后dict转换\n",
    "contact = (['张三',18],['李四',25])\n",
    "cap_dict = dict(contact)\n",
    "print(cap_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### keys(),values(),items()使用方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['张三', '李四'])\n",
      "dict_values([18, 25])\n",
      "dict_items([('张三', 18), ('李四', 25)])\n"
     ]
    }
   ],
   "source": [
    "# key() 获取字典内所有健\n",
    "print(test_dict.keys())\n",
    "# values() 获取字典内所有值\n",
    "print(test_dict.values())\n",
    "# items() 获取一组组的键值对\n",
    "print(test_dict.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 元祖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 元祖使用(),且不能修改元祖的元素"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 新建元祖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 3)\n",
      "('a', 'b', 'c')\n"
     ]
    }
   ],
   "source": [
    "tup = (1,2,3)\n",
    "print(tup)\n",
    "tup_lil = ('a','b','c')\n",
    "print(tup_lil)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取元祖长度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tup)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取元祖内元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "(2, 3)\n"
     ]
    }
   ],
   "source": [
    "# 普通索引\n",
    "print(tup[2])\n",
    "# 切片索引\n",
    "print(tup[1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 元祖与列表转化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3]\n",
      "('a', 'b', 'c')\n"
     ]
    }
   ],
   "source": [
    "# list() 将元祖转化为列表\n",
    "print(list(tup))\n",
    "# tuple() 将列表转化为元祖\n",
    "t_list = ['a','b','c']\n",
    "print(tuple(t_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### zip()函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 'a')\n",
      "(2, 'b')\n",
      "(3, 'c')\n",
      "(4, 'd')\n",
      "------------\n",
      "('a', 1)\n",
      "('b', 2)\n",
      "('c', 3)\n",
      "('d', 4)\n"
     ]
    }
   ],
   "source": [
    "# zip()用于将可迭代对象（列表、元祖）作为参数，将对象中对应元素打包成一个个元祖，返回这些元祖组成的列表\n",
    "# zip() 常与 for 循环搭配使用\n",
    "# 当可迭代对象是列表\n",
    "list_a = [1,2,3,4]\n",
    "list_b = ['a','b','c','d','e']\n",
    "for i in zip(list_a,list_b):  #有顺序区分\n",
    "    print(i)\n",
    "print('------------')\n",
    "for j in zip(list_b,list_a):\n",
    "    print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 'a')\n",
      "(2, 'b')\n",
      "(3, 'c')\n",
      "(4, 'd')\n",
      "-----------\n",
      "('a', 1)\n",
      "('b', 2)\n",
      "('c', 3)\n",
      "('d', 4)\n"
     ]
    }
   ],
   "source": [
    "# 当可迭代对象是元祖\n",
    "tup_a = (1,2,3,4)\n",
    "tup_b = ('a','b','c','d')\n",
    "for i in zip(tup_a,tup_b):\n",
    "    print(i)\n",
    "print('-----------')\n",
    "for j in zip(tup_b,tup_a):\n",
    "    print(j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 运算符"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 算术运算符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "100000000000000000000\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# % 返回两数相除的余数\n",
    "print(10%20)\n",
    "# ** 返回 x 的 y 次幂\n",
    "print(10**20)\n",
    "# //返回两数相除以后商的整数部分\n",
    "print(10//3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 比较运算符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# == 等于\n",
    "# != 不等于\n",
    "# >= 大于等于\n",
    "# <= 小于等于\n",
    "print(10 != 20)\n",
    "print(10 >= 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 逻辑运算符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n",
      "True\n",
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# and ：a and b，a和b同时为真，结果为真\n",
    "print((10 != 20)and(10 <= 20))\n",
    "print((10 != 20)and(10 >= 20))\n",
    "# or : a or b，a和b只要有一个为真，结果为真\n",
    "print((10 != 20)or(10 >= 20))\n",
    "# not : not a，如果a为真，则返回False，否则返回True\n",
    "print(not (10 != 20))\n",
    "print(not (10 == 20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 循环语句"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### for 循坏"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我想吃烧烤\n",
      "我想吃火锅\n",
      "我想吃锅包肉\n"
     ]
    }
   ],
   "source": [
    "# for 循环用来遍历任何序列的项目\n",
    "food = ['烧烤','火锅','锅包肉']\n",
    "for fo in food:\n",
    "    print('我想吃{}'.format(fo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### while 循环"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我已经减肥1天了\n",
      "我已经减肥2天了\n",
      "我已经减肥3天了\n",
      "我已经减肥4天了\n",
      "我已经减肥5天了\n",
      "我已经减肥6天了\n",
      "我已经减肥7天了\n",
      "我已经减肥7天了，可以去大吃一顿啦\n"
     ]
    }
   ],
   "source": [
    "# while 循环用来执行程序，当条件满足时，一直执行程序，直到程序不满足，终止程序\n",
    "day = 1\n",
    "while day <= 7:\n",
    "    print('我已经减肥{}天了'.format(day))\n",
    "    day += 1\n",
    "print('我已经减肥{}天了，可以去大吃一顿啦'.format(day-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 条件语句"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### if & else 语句"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "减肥成功\n"
     ]
    }
   ],
   "source": [
    "is_day = 10\n",
    "if is_day == 10:\n",
    "    print('减肥成功')\n",
    "else :\n",
    "    print('大胖子，你失败啦')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "减肥成功\n"
     ]
    }
   ],
   "source": [
    "is_day = 10\n",
    "if is_day == 0:\n",
    "    print('大胖子，你失败啦')\n",
    "else :\n",
    "    print('减肥成功')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### elif 语句"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80~100分\n"
     ]
    }
   ],
   "source": [
    "score = 89\n",
    "if score < 60:\n",
    "    print('小于60分')\n",
    "elif score < 70:\n",
    "    print('60~70分')\n",
    "elif score < 80:\n",
    "    print('70~80分')\n",
    "else :\n",
    "    print('80~100分')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 普通函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我在家里上学python\n",
      "我在公司上学python\n",
      "-----------\n",
      "我在家里上学python\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# 普通函数由函数名（必需）、参数、语句块（必需）、return、变量 组成\n",
    "def learn_python(location):\n",
    "    print('我在{}上学python'.format(location))\n",
    "learn_python('家里')\n",
    "learn_python('公司')\n",
    "print('-----------')\n",
    "print(learn_python('家里'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我在家里上学python\n",
      "我在公司上学python\n"
     ]
    }
   ],
   "source": [
    "# 利用return 对语句块的运行结果进行返回\n",
    "def learn_python(location):\n",
    "    doing = ('我在{}上学python'.format(location)) #将运行结果赋值给doing\n",
    "    return doing\n",
    "print(learn_python('家里'))\n",
    "print(learn_python('公司'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我在家里上学python，人就我一个\n",
      "我在公司上学python，人不多\n"
     ]
    }
   ],
   "source": [
    "# 定义含有多个参数的函数\n",
    "def learn_python(location,person):\n",
    "    doing = ('我在{}上学python，人{}'.format(location,person)) #将运行结果赋值给doing\n",
    "    return doing\n",
    "print(learn_python('家里','就我一个'))\n",
    "print(learn_python('公司','不多'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 匿名函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lambda 只是一个表达式，没有函数体\n",
    "# lambda arg1,arg2,arg3,.........:expression  (arg表示具体的参数，expression表示参数要执行的操作)\n",
    "f = lambda x,y:x+y\n",
    "f(1,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 高级特性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 列表生成式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 9, 16, 25]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = [1,2,3,4,5]\n",
    "[i**2 for i in num]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Aa', 'Ab', 'Ac', 'Ba', 'Bb', 'Bc', 'Ca', 'Cb', 'Cc']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 普通方法实现\n",
    "list1 = ['A','B','C']\n",
    "list2 = ['a','b','c']\n",
    "new = []\n",
    "for m in list1:\n",
    "    for n in list2:\n",
    "        new.append(m+n)\n",
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Aa', 'Ab', 'Ac', 'Ba', 'Bb', 'Bc', 'Ca', 'Cb', 'Cc']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 列表生成式\n",
    "list1 = ['A','B','C']\n",
    "list2 = ['a','b','c']\n",
    "[m+n for m in list1 for n in list2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### map 函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<map object at 0x102c7e4e0>\n",
      "4\n",
      "4\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# map 函数的表现形式是 map(function,agrs),表示对序列args中的每个值进行function操作，最终得到一个结果序列\n",
    "a = map(lambda x,y:x+y,[1,2,3],[3,2,1])\n",
    "print(a)          # map函数生成的结果不会直接显示，想要获取结果需要for 循环遍历\n",
    "for i in a:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 4, 4]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# map函数可以直接使用list方法，将结果生成一个列表\n",
    "b = list(map(lambda x,y:x+y,[1,2,3],[3,2,1]))\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' \\nimport module_name  直接import 具体的模块名\\n\\nfrom module1 import module2  从一个较大模块中import较小模块\\n'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入模块的两张方式\n",
    "''' \n",
    "import module_name  直接import 具体的模块名\n",
    "\n",
    "from module1 import module2  从一个较大模块中import较小模块\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas 数据结构\n",
    "  #模块导入\n",
    "  #import pandas as pd \n",
    "  #import numpy as np\n",
    "  #import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series 数据结构\n",
    "### 创建Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "3    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 传入一个列表\n",
    "# 直接传入一个列表会使用默认索引\n",
    "import pandas as pd\n",
    "S1 = pd.Series(['a','b','c','d'])\n",
    "S1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index 参数自定义索引\n",
    "S2 = pd.Series([1,2,3,4],index = ['a','b','c','d'])\n",
    "S2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "2    b\n",
       "3    c\n",
       "4    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S3 = pd.Series(['a','b','c','d'],index = [1,2,3,4])\n",
    "S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 传入字典\n",
    "S4 = pd.Series({'a':1,'b':2,'c':3,'d':4})\n",
    "S4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "2    b\n",
       "3    c\n",
       "4    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S5 = pd.Series({1:'a',2:'b',3:'c',4:'d'})\n",
    "S5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### index方法获取Series的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=4, step=1)\n",
      "Index(['a', 'b', 'c', 'd'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(S1.index)\n",
    "print(S2.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### values方法获取Series值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a' 'b' 'c' 'd']\n",
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "print(S1.values)\n",
    "print(S2.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame 表格型数据结构\n",
    "### 创建 DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0\n",
       "0  a\n",
       "1  b\n",
       "2  c\n",
       "3  d"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 传入一个列表\n",
    "import pandas as pd\n",
    "df1 = pd.DataFrame(['a','b','c','d'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1\n",
       "0  a  A\n",
       "1  b  B\n",
       "2  c  C\n",
       "3  d  D"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 传入一个嵌套列表\n",
    "df2 = pd.DataFrame([['a','A'],['b','B'],['c','C'],['d','D']])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d</td>\n",
       "      <td>D</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   0  1\n",
       "0  a  A\n",
       "1  b  B\n",
       "2  c  C\n",
       "3  d  D"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#嵌套可以是列表也可以是元祖\n",
    "df3 = pd.DataFrame([('a','A'),('b','B'),('c','C'),('d','D')])\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>小写</th>\n",
       "      <th>大写</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
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       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d</td>\n",
       "      <td>D</td>\n",
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       "</table>\n",
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      "text/plain": [
       "  小写 大写\n",
       "0  a  A\n",
       "1  b  B\n",
       "2  c  C\n",
       "3  d  D"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# 设置columns自定义列索引，index自定义行索引\n",
    "df4 = pd.DataFrame([('a','A'),('b','B'),('c','C'),('d','D')],columns = ['小写','大写'])\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一</th>\n",
       "      <td>a</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>二</th>\n",
       "      <td>b</td>\n",
       "      <td>B</td>\n",
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       "    <tr>\n",
       "      <th>三</th>\n",
       "      <td>c</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>四</th>\n",
       "      <td>d</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   0  1\n",
       "一  a  A\n",
       "二  b  B\n",
       "三  c  C\n",
       "四  d  D"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = pd.DataFrame([('a','A'),('b','B'),('c','C'),('d','D')],index = ['一','二','三','四'])\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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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       "      <th>小写</th>\n",
       "      <th>大写</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一</th>\n",
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       "      <td>C</td>\n",
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       "      <th>四</th>\n",
       "      <td>d</td>\n",
       "      <td>D</td>\n",
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       "</table>\n",
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      "text/plain": [
       "  小写 大写\n",
       "一  a  A\n",
       "二  b  B\n",
       "三  c  C\n",
       "四  d  D"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = pd.DataFrame([('a','A'),('b','B'),('c','C'),('d','D')],columns = ['小写','大写'],index = ['一','二','三','四'])\n",
    "df6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>小写</th>\n",
       "      <th>大写</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  小写 大写\n",
       "0  a  A\n",
       "1  b  B\n",
       "2  c  C\n",
       "3  d  D"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 传入一个字典\n",
    "data = {'小写':['a','b','c','d'],'大写':['A','B','C','D']}\n",
    "df7 = pd.DataFrame(data)\n",
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>小写</th>\n",
       "      <th>大写</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一</th>\n",
       "      <td>a</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>二</th>\n",
       "      <td>b</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>三</th>\n",
       "      <td>c</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四</th>\n",
       "      <td>d</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  小写 大写\n",
       "一  a  A\n",
       "二  b  B\n",
       "三  c  C\n",
       "四  d  D"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'小写':['a','b','c','d'],'大写':['A','B','C','D']}\n",
    "df8 = pd.DataFrame(data,index = ['一','二','三','四'])\n",
    "df8 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取DataFrame 的行、列索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['小写', '大写'], dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# columns 获取列索引\n",
    "df8.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['一', '二', '三', '四'], dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index 获取行索引\n",
    "df8.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 获取数据源\n",
    "## 导入外部数据\n",
    "    导入数据主要用到Pandas的read_x()方法，x代表代导入文件格式\n",
    "### 导入.xlsx文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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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       "        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>日期</th>\n",
       "      <th>安装量</th>\n",
       "      <th>次日</th>\n",
       "      <th>2日</th>\n",
       "      <th>3日</th>\n",
       "      <th>4日</th>\n",
       "      <th>5日</th>\n",
       "      <th>6日</th>\n",
       "      <th>7日</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-02-19</td>\n",
       "      <td>10</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-02-20</td>\n",
       "      <td>156</td>\n",
       "      <td>0.4359</td>\n",
       "      <td>0.2628</td>\n",
       "      <td>0.1795</td>\n",
       "      <td>0.1346</td>\n",
       "      <td>0.1987</td>\n",
       "      <td>0.1603</td>\n",
       "      <td>0.1795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-02-21</td>\n",
       "      <td>708</td>\n",
       "      <td>0.3858</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.2057</td>\n",
       "      <td>0.1801</td>\n",
       "      <td>0.1674</td>\n",
       "      <td>0.1461</td>\n",
       "      <td>0.1319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-02-22</td>\n",
       "      <td>987</td>\n",
       "      <td>0.3953</td>\n",
       "      <td>0.2564</td>\n",
       "      <td>0.2257</td>\n",
       "      <td>0.1910</td>\n",
       "      <td>0.1655</td>\n",
       "      <td>0.1410</td>\n",
       "      <td>0.1348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-02-23</td>\n",
       "      <td>931</td>\n",
       "      <td>0.4423</td>\n",
       "      <td>0.3204</td>\n",
       "      <td>0.2589</td>\n",
       "      <td>0.2276</td>\n",
       "      <td>0.2093</td>\n",
       "      <td>0.1834</td>\n",
       "      <td>0.1683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-02-24</td>\n",
       "      <td>1100</td>\n",
       "      <td>0.4562</td>\n",
       "      <td>0.3358</td>\n",
       "      <td>0.2445</td>\n",
       "      <td>0.2354</td>\n",
       "      <td>0.1998</td>\n",
       "      <td>0.1934</td>\n",
       "      <td>0.1624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-02-25</td>\n",
       "      <td>1054</td>\n",
       "      <td>0.4500</td>\n",
       "      <td>0.3587</td>\n",
       "      <td>0.3111</td>\n",
       "      <td>0.2588</td>\n",
       "      <td>0.2455</td>\n",
       "      <td>0.2226</td>\n",
       "      <td>0.1865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-02-26</td>\n",
       "      <td>1125</td>\n",
       "      <td>0.4468</td>\n",
       "      <td>0.3735</td>\n",
       "      <td>0.3012</td>\n",
       "      <td>0.2690</td>\n",
       "      <td>0.2163</td>\n",
       "      <td>0.2029</td>\n",
       "      <td>0.1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-02-27</td>\n",
       "      <td>488</td>\n",
       "      <td>0.3648</td>\n",
       "      <td>0.3115</td>\n",
       "      <td>0.2480</td>\n",
       "      <td>0.1967</td>\n",
       "      <td>0.2090</td>\n",
       "      <td>0.2049</td>\n",
       "      <td>0.1557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>95</td>\n",
       "      <td>0.2766</td>\n",
       "      <td>0.2340</td>\n",
       "      <td>0.2128</td>\n",
       "      <td>0.1383</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.1383</td>\n",
       "      <td>0.1489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>57</td>\n",
       "      <td>0.3393</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.1429</td>\n",
       "      <td>0.1964</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>0.0714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-03-02</td>\n",
       "      <td>58</td>\n",
       "      <td>0.1930</td>\n",
       "      <td>0.3158</td>\n",
       "      <td>0.2281</td>\n",
       "      <td>0.2281</td>\n",
       "      <td>0.1404</td>\n",
       "      <td>0.2105</td>\n",
       "      <td>0.1579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-03-03</td>\n",
       "      <td>39</td>\n",
       "      <td>0.3421</td>\n",
       "      <td>0.2105</td>\n",
       "      <td>0.2632</td>\n",
       "      <td>0.2105</td>\n",
       "      <td>0.2105</td>\n",
       "      <td>0.2105</td>\n",
       "      <td>0.2105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>25</td>\n",
       "      <td>0.3600</td>\n",
       "      <td>0.2400</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>0.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019-03-05</td>\n",
       "      <td>26</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-03-06</td>\n",
       "      <td>20</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.3500</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-03-07</td>\n",
       "      <td>19</td>\n",
       "      <td>0.2632</td>\n",
       "      <td>0.1053</td>\n",
       "      <td>0.1579</td>\n",
       "      <td>0.2105</td>\n",
       "      <td>0.1053</td>\n",
       "      <td>0.2632</td>\n",
       "      <td>0.2105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-03-08</td>\n",
       "      <td>25</td>\n",
       "      <td>0.2917</td>\n",
       "      <td>0.2083</td>\n",
       "      <td>0.1667</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.2083</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.1667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>20</td>\n",
       "      <td>0.1579</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1579</td>\n",
       "      <td>0.0526</td>\n",
       "      <td>0.0526</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019-03-10</td>\n",
       "      <td>17</td>\n",
       "      <td>0.1176</td>\n",
       "      <td>0.1176</td>\n",
       "      <td>0.1765</td>\n",
       "      <td>0.0588</td>\n",
       "      <td>0.0588</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019-03-11</td>\n",
       "      <td>14</td>\n",
       "      <td>0.3571</td>\n",
       "      <td>0.2857</td>\n",
       "      <td>0.0714</td>\n",
       "      <td>0.1429</td>\n",
       "      <td>0.0714</td>\n",
       "      <td>0.2143</td>\n",
       "      <td>0.2143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019-03-12</td>\n",
       "      <td>15</td>\n",
       "      <td>0.1429</td>\n",
       "      <td>0.2857</td>\n",
       "      <td>0.2857</td>\n",
       "      <td>0.0714</td>\n",
       "      <td>0.1429</td>\n",
       "      <td>0.2143</td>\n",
       "      <td>0.4286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019-03-13</td>\n",
       "      <td>13</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019-03-14</td>\n",
       "      <td>5481</td>\n",
       "      <td>0.5090</td>\n",
       "      <td>0.3735</td>\n",
       "      <td>0.3113</td>\n",
       "      <td>0.2740</td>\n",
       "      <td>0.2477</td>\n",
       "      <td>0.2188</td>\n",
       "      <td>0.1949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>8691</td>\n",
       "      <td>0.5158</td>\n",
       "      <td>0.3778</td>\n",
       "      <td>0.3184</td>\n",
       "      <td>0.2713</td>\n",
       "      <td>0.2370</td>\n",
       "      <td>0.2116</td>\n",
       "      <td>0.1931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-03-16</td>\n",
       "      <td>8119</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.3793</td>\n",
       "      <td>0.3196</td>\n",
       "      <td>0.2793</td>\n",
       "      <td>0.2468</td>\n",
       "      <td>0.2171</td>\n",
       "      <td>0.2111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-03-17</td>\n",
       "      <td>8412</td>\n",
       "      <td>0.4990</td>\n",
       "      <td>0.3838</td>\n",
       "      <td>0.3139</td>\n",
       "      <td>0.2752</td>\n",
       "      <td>0.2454</td>\n",
       "      <td>0.2212</td>\n",
       "      <td>0.2041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-03-18</td>\n",
       "      <td>10461</td>\n",
       "      <td>0.5198</td>\n",
       "      <td>0.4020</td>\n",
       "      <td>0.3246</td>\n",
       "      <td>0.2834</td>\n",
       "      <td>0.2461</td>\n",
       "      <td>0.2320</td>\n",
       "      <td>0.2198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-03-19</td>\n",
       "      <td>11094</td>\n",
       "      <td>0.5107</td>\n",
       "      <td>0.3904</td>\n",
       "      <td>0.3193</td>\n",
       "      <td>0.2765</td>\n",
       "      <td>0.2507</td>\n",
       "      <td>0.2326</td>\n",
       "      <td>0.2158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-03-20</td>\n",
       "      <td>11149</td>\n",
       "      <td>0.5096</td>\n",
       "      <td>0.3929</td>\n",
       "      <td>0.3308</td>\n",
       "      <td>0.2856</td>\n",
       "      <td>0.2670</td>\n",
       "      <td>0.2376</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-03-21</td>\n",
       "      <td>11229</td>\n",
       "      <td>0.5076</td>\n",
       "      <td>0.3838</td>\n",
       "      <td>0.3236</td>\n",
       "      <td>0.2868</td>\n",
       "      <td>0.2606</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>13397</td>\n",
       "      <td>0.5087</td>\n",
       "      <td>0.3922</td>\n",
       "      <td>0.3339</td>\n",
       "      <td>0.2992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019-03-23</td>\n",
       "      <td>15799</td>\n",
       "      <td>0.5043</td>\n",
       "      <td>0.3694</td>\n",
       "      <td>0.3167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2019-03-24</td>\n",
       "      <td>17638</td>\n",
       "      <td>0.4592</td>\n",
       "      <td>0.3460</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>34</th>\n",
       "      <td>2019-03-25</td>\n",
       "      <td>15538</td>\n",
       "      <td>0.4978</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>35</th>\n",
       "      <td>2019-03-26</td>\n",
       "      <td>16991</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           日期    安装量      次日      2日      3日      4日      5日      6日      7日\n",
       "0  2019-02-19     10  0.1250  0.0000  0.1250  0.0000     NaN  0.1250  0.1250\n",
       "1  2019-02-20    156  0.4359  0.2628  0.1795  0.1346  0.1987  0.1603  0.1795\n",
       "2  2019-02-21    708  0.3858  0.2255  0.2057  0.1801  0.1674  0.1461  0.1319\n",
       "3  2019-02-22    987  0.3953  0.2564  0.2257  0.1910  0.1655  0.1410  0.1348\n",
       "4  2019-02-23    931  0.4423  0.3204  0.2589  0.2276  0.2093  0.1834  0.1683\n",
       "5  2019-02-24   1100  0.4562  0.3358  0.2445  0.2354  0.1998  0.1934  0.1624\n",
       "6  2019-02-25   1054  0.4500  0.3587  0.3111  0.2588  0.2455  0.2226  0.1865\n",
       "7  2019-02-26   1125  0.4468  0.3735  0.3012  0.2690  0.2163  0.2029  0.1939\n",
       "8  2019-02-27    488  0.3648  0.3115  0.2480  0.1967  0.2090  0.2049  0.1557\n",
       "9  2019-02-28     95  0.2766  0.2340  0.2128  0.1383  0.1809  0.1383  0.1489\n",
       "10 2019-03-01     57  0.3393  0.2500  0.1429  0.1964  0.1250  0.1071  0.0714\n",
       "11 2019-03-02     58  0.1930  0.3158  0.2281  0.2281  0.1404  0.2105  0.1579\n",
       "12 2019-03-03     39  0.3421  0.2105  0.2632  0.2105  0.2105  0.2105  0.2105\n",
       "13 2019-03-04     25  0.3600  0.2400  0.2000  0.1600  0.0400  0.1200  0.0400\n",
       "14 2019-03-05     26  0.2000  0.1600  0.1600  0.1200  0.1200  0.2000  0.1200\n",
       "15 2019-03-06     20  0.3000  0.2000  0.4000  0.2500  0.3500  0.3000  0.3000\n",
       "16 2019-03-07     19  0.2632  0.1053  0.1579  0.2105  0.1053  0.2632  0.2105\n",
       "17 2019-03-08     25  0.2917  0.2083  0.1667  0.3333  0.2083  0.1250  0.1667\n",
       "18 2019-03-09     20  0.1579     NaN  0.1579  0.0526  0.0526     NaN     NaN\n",
       "19 2019-03-10     17  0.1176  0.1176  0.1765  0.0588  0.0588  0.0000  0.1176\n",
       "20 2019-03-11     14  0.3571  0.2857  0.0714  0.1429  0.0714  0.2143  0.2143\n",
       "21 2019-03-12     15  0.1429  0.2857  0.2857  0.0714  0.1429  0.2143  0.4286\n",
       "22 2019-03-13     13  0.3000  0.3000  0.2000  0.3000  0.3000  0.2000  0.4000\n",
       "23 2019-03-14   5481  0.5090  0.3735  0.3113  0.2740  0.2477  0.2188  0.1949\n",
       "24 2019-03-15   8691  0.5158  0.3778  0.3184  0.2713  0.2370  0.2116  0.1931\n",
       "25 2019-03-16   8119  0.5140  0.3793  0.3196  0.2793  0.2468  0.2171  0.2111\n",
       "26 2019-03-17   8412  0.4990  0.3838  0.3139  0.2752  0.2454  0.2212  0.2041\n",
       "27 2019-03-18  10461  0.5198  0.4020  0.3246  0.2834  0.2461  0.2320  0.2198\n",
       "28 2019-03-19  11094  0.5107  0.3904  0.3193  0.2765  0.2507  0.2326  0.2158\n",
       "29 2019-03-20  11149  0.5096  0.3929  0.3308  0.2856  0.2670  0.2376     NaN\n",
       "30 2019-03-21  11229  0.5076  0.3838  0.3236  0.2868  0.2606     NaN     NaN\n",
       "31 2019-03-22  13397  0.5087  0.3922  0.3339  0.2992     NaN     NaN     NaN\n",
       "32 2019-03-23  15799  0.5043  0.3694  0.3167     NaN     NaN     NaN     NaN\n",
       "33 2019-03-24  17638  0.4592  0.3460     NaN     NaN     NaN     NaN     NaN\n",
       "34 2019-03-25  15538  0.4978     NaN     NaN     NaN     NaN     NaN     NaN\n",
       "35 2019-03-26  16991     NaN     NaN     NaN     NaN     NaN     NaN     NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_excel(r'/Users/mc-016/Desktop/toycrushbrick.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           日期    DAU     MAU    用户黏度    DNU    DOU    DCU  DPU  付费次数  首次付费用户数  \\\n",
      "0  2019-02-19     14      30  0.4667     10      6     -1    0     0        0   \n",
      "1  2019-02-20    164     190  0.8632    156      8      6    0     0        0   \n",
      "2  2019-02-21    796     911  0.8738    708     93     71    0     0        0   \n",
      "3  2019-02-22   1341    1930  0.6948    987    367    429    1     1        1   \n",
      "4  2019-02-23   1566    2901  0.5398    931    642    699    0     0        0   \n",
      "5  2019-02-24   1970    4019  0.4902   1100    876    690    3     4        3   \n",
      "6  2019-02-25   2292    5111  0.4484   1054   1243    727    1     1        1   \n",
      "7  2019-02-26   2583    6251  0.4132   1125   1467    825    0     0        0   \n",
      "8  2019-02-27   2210    6779  0.3260    488   1724    859    0     0        0   \n",
      "9  2019-02-28   1789    6893  0.2595     95   1695    515    0     0        0   \n",
      "10 2019-03-01   1495    6953  0.2150     57   1440    349    0     0        0   \n",
      "11 2019-03-02   1383    7012  0.1972     58   1326    169    0     0        0   \n",
      "12 2019-03-03   1214    7054  0.1721     39   1176    207    0     0        0   \n",
      "13 2019-03-04   1126    7087  0.1589     25   1101    113    0     0        0   \n",
      "14 2019-03-05   1043    7112  0.1467     26   1018    108    0     0        0   \n",
      "15 2019-03-06    941    7136  0.1319     20    921    122    0     0        0   \n",
      "16 2019-03-07    920    7160  0.1285     19    901     40    0     0        0   \n",
      "17 2019-03-08    846    7182  0.1178     25    822     98    0     0        0   \n",
      "18 2019-03-09    772    7196  0.1073     20    754     92    0     0        0   \n",
      "19 2019-03-10    738    7212  0.1023     17    721     51    0     0        0   \n",
      "20 2019-03-11    810    7227  0.1121     14    796    -58    0     0        0   \n",
      "21 2019-03-12    725    7242  0.1001     15    711     99    0     0        0   \n",
      "22 2019-03-13    695    7253  0.0958     13    685     40    0     0        0   \n",
      "23 2019-03-14   6183   12761  0.4845   5481    720    -25    4     5        4   \n",
      "24 2019-03-15  12217   21519  0.5677   8691   3548   2635   13    17       11   \n",
      "25 2019-03-16  15363   29692  0.5174   8119   7266   4951   11    12        9   \n",
      "26 2019-03-17  18320   38164  0.4800   8412   9934   5429   18    25       14   \n",
      "27 2019-03-18  22728   48737  0.4663  10461  12294   6026   24    31       17   \n",
      "28 2019-03-19  26864   59949  0.4481  11094  15810   6918   20    22       15   \n",
      "29 2019-03-20  29920   71169  0.4204  11149  18827   8037   20    24       11   \n",
      "30 2019-03-21  32718   82582  0.3962  11229  21542   8378   27    32       21   \n",
      "31 2019-03-22  37314   96049  0.3885  13397  23974   8744   28    38       15   \n",
      "32 2019-03-23  43041  111701  0.3853  15799  27298  10016   34    47       26   \n",
      "33 2019-03-24  49339  129107  0.3822  17638  31777  11264   27    35       18   \n",
      "34 2019-03-25  51121  144377  0.3541  15538  35669  13670   31    42       21   \n",
      "\n",
      "      ...     官方流水($)  广告收入($)  插屏广告收入($)  激励视频收入($)  广告占比(%)  付费率(%)  \\\n",
      "0     ...        0.00     0.00       0.00       0.00   0.0000  0.0000   \n",
      "1     ...        0.00     2.38       0.09       2.29   1.0000  0.0000   \n",
      "2     ...        0.00     6.25       0.59       5.66   1.0000  0.0000   \n",
      "3     ...        1.99    16.75       7.99       8.76   0.8938  0.0007   \n",
      "4     ...        0.00    41.66      19.01      22.65   1.0000  0.0000   \n",
      "5     ...        6.96    61.63      24.57      37.06   0.8985  0.0015   \n",
      "6     ...        1.99    49.38      31.08      18.30   0.9613  0.0004   \n",
      "7     ...        0.00    67.08      32.50      34.58   1.0000  0.0000   \n",
      "8     ...        0.00    60.56      33.89      26.67   1.0000  0.0000   \n",
      "9     ...        0.00    53.70      24.19      29.51   1.0000  0.0000   \n",
      "10    ...        0.00    57.88      29.20      28.68   1.0000  0.0000   \n",
      "11    ...        0.00    58.48      27.69      30.79   1.0000  0.0000   \n",
      "12    ...        0.00    52.40      27.26      25.14   1.0000  0.0000   \n",
      "13    ...        0.00    40.32      22.36      17.96   1.0000  0.0000   \n",
      "14    ...        0.00    37.80      20.06      17.74   1.0000  0.0000   \n",
      "15    ...        0.00    35.86      16.32      19.54   1.0000  0.0000   \n",
      "16    ...        0.00    27.61      16.07      11.54   1.0000  0.0000   \n",
      "17    ...        0.00    35.46      14.14      21.32   1.0000  0.0000   \n",
      "18    ...        0.00    19.61       9.24      10.37   1.0000  0.0000   \n",
      "19    ...        0.00    26.23      12.25      13.98   1.0000  0.0000   \n",
      "20    ...        0.00    21.49       9.35      12.14   1.0000  0.0000   \n",
      "21    ...        0.00    23.84       9.85      13.99   1.0000  0.0000   \n",
      "22    ...        0.00    26.77      14.46      12.31   1.0000  0.0000   \n",
      "23    ...       28.95   493.01     278.69     214.32   0.9445  0.0006   \n",
      "24    ...       58.83  1030.41     587.68     442.73   0.9460  0.0011   \n",
      "25    ...       45.88  1451.98     855.96     596.02   0.9694  0.0007   \n",
      "26    ...      114.75  1829.71    1086.71     743.00   0.9410  0.0010   \n",
      "27    ...      109.69  2444.21    1479.50     964.71   0.9571  0.0011   \n",
      "28    ...       87.78  2642.59    1689.18     953.41   0.9679  0.0007   \n",
      "29    ...      144.76  2833.31    1818.63    1014.68   0.9514  0.0007   \n",
      "30    ...      119.68  2669.90    1399.98    1269.92   0.9571  0.0008   \n",
      "31    ...      174.62  3182.25    1627.08    1555.17   0.9480  0.0008   \n",
      "32    ...      202.53  3457.10    1703.23    1753.87   0.9447  0.0008   \n",
      "33    ...      241.65  3874.87    1946.20    1928.67   0.9413  0.0005   \n",
      "34    ...      259.58  4040.29    1839.50    2200.79   0.9396  0.0006   \n",
      "\n",
      "    内购ARPU($)  广告ARPU($)  总ARPU($)  ARPPU($)  \n",
      "0      0.0000     0.0000    0.0000    0.0000  \n",
      "1      0.0000     0.0145    0.0145    0.0000  \n",
      "2      0.0000     0.0079    0.0079    0.0000  \n",
      "3      0.0015     0.0125    0.0140    1.9900  \n",
      "4      0.0000     0.0266    0.0266    0.0000  \n",
      "5      0.0035     0.0313    0.0348    2.3200  \n",
      "6      0.0009     0.0215    0.0224    1.9900  \n",
      "7      0.0000     0.0260    0.0260    0.0000  \n",
      "8      0.0000     0.0274    0.0274    0.0000  \n",
      "9      0.0000     0.0300    0.0300    0.0000  \n",
      "10     0.0000     0.0387    0.0387    0.0000  \n",
      "11     0.0000     0.0423    0.0423    0.0000  \n",
      "12     0.0000     0.0432    0.0432    0.0000  \n",
      "13     0.0000     0.0358    0.0358    0.0000  \n",
      "14     0.0000     0.0362    0.0362    0.0000  \n",
      "15     0.0000     0.0381    0.0381    0.0000  \n",
      "16     0.0000     0.0300    0.0300    0.0000  \n",
      "17     0.0000     0.0419    0.0419    0.0000  \n",
      "18     0.0000     0.0254    0.0254    0.0000  \n",
      "19     0.0000     0.0355    0.0355    0.0000  \n",
      "20     0.0000     0.0265    0.0265    0.0000  \n",
      "21     0.0000     0.0329    0.0329    0.0000  \n",
      "22     0.0000     0.0385    0.0385    0.0000  \n",
      "23     0.0047     0.0797    0.0844    7.2375  \n",
      "24     0.0048     0.0843    0.0892    4.5254  \n",
      "25     0.0030     0.0945    0.0975    4.1709  \n",
      "26     0.0063     0.0999    0.1061    6.3750  \n",
      "27     0.0048     0.1075    0.1124    4.5704  \n",
      "28     0.0033     0.0984    0.1016    4.3890  \n",
      "29     0.0048     0.0947    0.0995    7.2380  \n",
      "30     0.0037     0.0816    0.0853    4.4326  \n",
      "31     0.0047     0.0853    0.0900    6.2364  \n",
      "32     0.0047     0.0803    0.0850    5.9568  \n",
      "33     0.0049     0.0785    0.0834    8.9500  \n",
      "34     0.0051     0.0790    0.0841    8.3735  \n",
      "\n",
      "[35 rows x 23 columns]\n",
      "-------\n",
      "           日期    DAU     MAU    用户黏度    DNU    DOU    DCU  DPU  付费次数  首次付费用户数  \\\n",
      "0  2019-02-19     14      30  0.4667     10      6     -1    0     0        0   \n",
      "1  2019-02-20    164     190  0.8632    156      8      6    0     0        0   \n",
      "2  2019-02-21    796     911  0.8738    708     93     71    0     0        0   \n",
      "3  2019-02-22   1341    1930  0.6948    987    367    429    1     1        1   \n",
      "4  2019-02-23   1566    2901  0.5398    931    642    699    0     0        0   \n",
      "5  2019-02-24   1970    4019  0.4902   1100    876    690    3     4        3   \n",
      "6  2019-02-25   2292    5111  0.4484   1054   1243    727    1     1        1   \n",
      "7  2019-02-26   2583    6251  0.4132   1125   1467    825    0     0        0   \n",
      "8  2019-02-27   2210    6779  0.3260    488   1724    859    0     0        0   \n",
      "9  2019-02-28   1789    6893  0.2595     95   1695    515    0     0        0   \n",
      "10 2019-03-01   1495    6953  0.2150     57   1440    349    0     0        0   \n",
      "11 2019-03-02   1383    7012  0.1972     58   1326    169    0     0        0   \n",
      "12 2019-03-03   1214    7054  0.1721     39   1176    207    0     0        0   \n",
      "13 2019-03-04   1126    7087  0.1589     25   1101    113    0     0        0   \n",
      "14 2019-03-05   1043    7112  0.1467     26   1018    108    0     0        0   \n",
      "15 2019-03-06    941    7136  0.1319     20    921    122    0     0        0   \n",
      "16 2019-03-07    920    7160  0.1285     19    901     40    0     0        0   \n",
      "17 2019-03-08    846    7182  0.1178     25    822     98    0     0        0   \n",
      "18 2019-03-09    772    7196  0.1073     20    754     92    0     0        0   \n",
      "19 2019-03-10    738    7212  0.1023     17    721     51    0     0        0   \n",
      "20 2019-03-11    810    7227  0.1121     14    796    -58    0     0        0   \n",
      "21 2019-03-12    725    7242  0.1001     15    711     99    0     0        0   \n",
      "22 2019-03-13    695    7253  0.0958     13    685     40    0     0        0   \n",
      "23 2019-03-14   6183   12761  0.4845   5481    720    -25    4     5        4   \n",
      "24 2019-03-15  12217   21519  0.5677   8691   3548   2635   13    17       11   \n",
      "25 2019-03-16  15363   29692  0.5174   8119   7266   4951   11    12        9   \n",
      "26 2019-03-17  18320   38164  0.4800   8412   9934   5429   18    25       14   \n",
      "27 2019-03-18  22728   48737  0.4663  10461  12294   6026   24    31       17   \n",
      "28 2019-03-19  26864   59949  0.4481  11094  15810   6918   20    22       15   \n",
      "29 2019-03-20  29920   71169  0.4204  11149  18827   8037   20    24       11   \n",
      "30 2019-03-21  32718   82582  0.3962  11229  21542   8378   27    32       21   \n",
      "31 2019-03-22  37314   96049  0.3885  13397  23974   8744   28    38       15   \n",
      "32 2019-03-23  43041  111701  0.3853  15799  27298  10016   34    47       26   \n",
      "33 2019-03-24  49339  129107  0.3822  17638  31777  11264   27    35       18   \n",
      "34 2019-03-25  51121  144377  0.3541  15538  35669  13670   31    42       21   \n",
      "\n",
      "      ...     官方流水($)  广告收入($)  插屏广告收入($)  激励视频收入($)  广告占比(%)  付费率(%)  \\\n",
      "0     ...        0.00     0.00       0.00       0.00   0.0000  0.0000   \n",
      "1     ...        0.00     2.38       0.09       2.29   1.0000  0.0000   \n",
      "2     ...        0.00     6.25       0.59       5.66   1.0000  0.0000   \n",
      "3     ...        1.99    16.75       7.99       8.76   0.8938  0.0007   \n",
      "4     ...        0.00    41.66      19.01      22.65   1.0000  0.0000   \n",
      "5     ...        6.96    61.63      24.57      37.06   0.8985  0.0015   \n",
      "6     ...        1.99    49.38      31.08      18.30   0.9613  0.0004   \n",
      "7     ...        0.00    67.08      32.50      34.58   1.0000  0.0000   \n",
      "8     ...        0.00    60.56      33.89      26.67   1.0000  0.0000   \n",
      "9     ...        0.00    53.70      24.19      29.51   1.0000  0.0000   \n",
      "10    ...        0.00    57.88      29.20      28.68   1.0000  0.0000   \n",
      "11    ...        0.00    58.48      27.69      30.79   1.0000  0.0000   \n",
      "12    ...        0.00    52.40      27.26      25.14   1.0000  0.0000   \n",
      "13    ...        0.00    40.32      22.36      17.96   1.0000  0.0000   \n",
      "14    ...        0.00    37.80      20.06      17.74   1.0000  0.0000   \n",
      "15    ...        0.00    35.86      16.32      19.54   1.0000  0.0000   \n",
      "16    ...        0.00    27.61      16.07      11.54   1.0000  0.0000   \n",
      "17    ...        0.00    35.46      14.14      21.32   1.0000  0.0000   \n",
      "18    ...        0.00    19.61       9.24      10.37   1.0000  0.0000   \n",
      "19    ...        0.00    26.23      12.25      13.98   1.0000  0.0000   \n",
      "20    ...        0.00    21.49       9.35      12.14   1.0000  0.0000   \n",
      "21    ...        0.00    23.84       9.85      13.99   1.0000  0.0000   \n",
      "22    ...        0.00    26.77      14.46      12.31   1.0000  0.0000   \n",
      "23    ...       28.95   493.01     278.69     214.32   0.9445  0.0006   \n",
      "24    ...       58.83  1030.41     587.68     442.73   0.9460  0.0011   \n",
      "25    ...       45.88  1451.98     855.96     596.02   0.9694  0.0007   \n",
      "26    ...      114.75  1829.71    1086.71     743.00   0.9410  0.0010   \n",
      "27    ...      109.69  2444.21    1479.50     964.71   0.9571  0.0011   \n",
      "28    ...       87.78  2642.59    1689.18     953.41   0.9679  0.0007   \n",
      "29    ...      144.76  2833.31    1818.63    1014.68   0.9514  0.0007   \n",
      "30    ...      119.68  2669.90    1399.98    1269.92   0.9571  0.0008   \n",
      "31    ...      174.62  3182.25    1627.08    1555.17   0.9480  0.0008   \n",
      "32    ...      202.53  3457.10    1703.23    1753.87   0.9447  0.0008   \n",
      "33    ...      241.65  3874.87    1946.20    1928.67   0.9413  0.0005   \n",
      "34    ...      259.58  4040.29    1839.50    2200.79   0.9396  0.0006   \n",
      "\n",
      "    内购ARPU($)  广告ARPU($)  总ARPU($)  ARPPU($)  \n",
      "0      0.0000     0.0000    0.0000    0.0000  \n",
      "1      0.0000     0.0145    0.0145    0.0000  \n",
      "2      0.0000     0.0079    0.0079    0.0000  \n",
      "3      0.0015     0.0125    0.0140    1.9900  \n",
      "4      0.0000     0.0266    0.0266    0.0000  \n",
      "5      0.0035     0.0313    0.0348    2.3200  \n",
      "6      0.0009     0.0215    0.0224    1.9900  \n",
      "7      0.0000     0.0260    0.0260    0.0000  \n",
      "8      0.0000     0.0274    0.0274    0.0000  \n",
      "9      0.0000     0.0300    0.0300    0.0000  \n",
      "10     0.0000     0.0387    0.0387    0.0000  \n",
      "11     0.0000     0.0423    0.0423    0.0000  \n",
      "12     0.0000     0.0432    0.0432    0.0000  \n",
      "13     0.0000     0.0358    0.0358    0.0000  \n",
      "14     0.0000     0.0362    0.0362    0.0000  \n",
      "15     0.0000     0.0381    0.0381    0.0000  \n",
      "16     0.0000     0.0300    0.0300    0.0000  \n",
      "17     0.0000     0.0419    0.0419    0.0000  \n",
      "18     0.0000     0.0254    0.0254    0.0000  \n",
      "19     0.0000     0.0355    0.0355    0.0000  \n",
      "20     0.0000     0.0265    0.0265    0.0000  \n",
      "21     0.0000     0.0329    0.0329    0.0000  \n",
      "22     0.0000     0.0385    0.0385    0.0000  \n",
      "23     0.0047     0.0797    0.0844    7.2375  \n",
      "24     0.0048     0.0843    0.0892    4.5254  \n",
      "25     0.0030     0.0945    0.0975    4.1709  \n",
      "26     0.0063     0.0999    0.1061    6.3750  \n",
      "27     0.0048     0.1075    0.1124    4.5704  \n",
      "28     0.0033     0.0984    0.1016    4.3890  \n",
      "29     0.0048     0.0947    0.0995    7.2380  \n",
      "30     0.0037     0.0816    0.0853    4.4326  \n",
      "31     0.0047     0.0853    0.0900    6.2364  \n",
      "32     0.0047     0.0803    0.0850    5.9568  \n",
      "33     0.0049     0.0785    0.0834    8.9500  \n",
      "34     0.0051     0.0790    0.0841    8.3735  \n",
      "\n",
      "[35 rows x 23 columns]\n"
     ]
    }
   ],
   "source": [
    "# 指定导入哪个Sheet，不指定sheet，默认第一个\n",
    "import pandas as pd\n",
    "df2 = pd.read_excel(r'/Users/mc-016/Desktop/toycrushbrick.xlsx',sheet_name = '收入')  #指定sheet名字\n",
    "df3 = pd.read_excel(r'/Users/mc-016/Desktop/toycrushbrick.xlsx',sheet_name = 1) #指定sheet顺序\n",
    "print(df2)\n",
    "print('-------')\n",
    "print(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DAU</th>\n",
       "      <th>MAU</th>\n",
       "      <th>用户黏度</th>\n",
       "      <th>DNU</th>\n",
       "      <th>DOU</th>\n",
       "      <th>DCU</th>\n",
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       "      <th>新增付费用户数</th>\n",
       "      <th>...</th>\n",
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       "      <th>激励视频收入($)</th>\n",
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       "      <th>内购ARPU($)</th>\n",
       "      <th>广告ARPU($)</th>\n",
       "      <th>总ARPU($)</th>\n",
       "      <th>ARPPU($)</th>\n",
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       "      <th>日期</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-02-19</th>\n",
       "      <td>14</td>\n",
       "      <td>30</td>\n",
       "      <td>0.4667</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>2019-02-20</th>\n",
       "      <td>164</td>\n",
       "      <td>190</td>\n",
       "      <td>0.8632</td>\n",
       "      <td>156</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>2.38</td>\n",
       "      <td>0.09</td>\n",
       "      <td>2.29</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-21</th>\n",
       "      <td>796</td>\n",
       "      <td>911</td>\n",
       "      <td>0.8738</td>\n",
       "      <td>708</td>\n",
       "      <td>93</td>\n",
       "      <td>71</td>\n",
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       "      <td>6.25</td>\n",
       "      <td>0.59</td>\n",
       "      <td>5.66</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-22</th>\n",
       "      <td>1341</td>\n",
       "      <td>1930</td>\n",
       "      <td>0.6948</td>\n",
       "      <td>987</td>\n",
       "      <td>367</td>\n",
       "      <td>429</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.99</td>\n",
       "      <td>16.75</td>\n",
       "      <td>7.99</td>\n",
       "      <td>8.76</td>\n",
       "      <td>0.8938</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>1.9900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-23</th>\n",
       "      <td>1566</td>\n",
       "      <td>2901</td>\n",
       "      <td>0.5398</td>\n",
       "      <td>931</td>\n",
       "      <td>642</td>\n",
       "      <td>699</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.66</td>\n",
       "      <td>19.01</td>\n",
       "      <td>22.65</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0266</td>\n",
       "      <td>0.0266</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-24</th>\n",
       "      <td>1970</td>\n",
       "      <td>4019</td>\n",
       "      <td>0.4902</td>\n",
       "      <td>1100</td>\n",
       "      <td>876</td>\n",
       "      <td>690</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6.96</td>\n",
       "      <td>61.63</td>\n",
       "      <td>24.57</td>\n",
       "      <td>37.06</td>\n",
       "      <td>0.8985</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.0313</td>\n",
       "      <td>0.0348</td>\n",
       "      <td>2.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-25</th>\n",
       "      <td>2292</td>\n",
       "      <td>5111</td>\n",
       "      <td>0.4484</td>\n",
       "      <td>1054</td>\n",
       "      <td>1243</td>\n",
       "      <td>727</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.99</td>\n",
       "      <td>49.38</td>\n",
       "      <td>31.08</td>\n",
       "      <td>18.30</td>\n",
       "      <td>0.9613</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0215</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>1.9900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-26</th>\n",
       "      <td>2583</td>\n",
       "      <td>6251</td>\n",
       "      <td>0.4132</td>\n",
       "      <td>1125</td>\n",
       "      <td>1467</td>\n",
       "      <td>825</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>67.08</td>\n",
       "      <td>32.50</td>\n",
       "      <td>34.58</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0260</td>\n",
       "      <td>0.0260</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-27</th>\n",
       "      <td>2210</td>\n",
       "      <td>6779</td>\n",
       "      <td>0.3260</td>\n",
       "      <td>488</td>\n",
       "      <td>1724</td>\n",
       "      <td>859</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>60.56</td>\n",
       "      <td>33.89</td>\n",
       "      <td>26.67</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0274</td>\n",
       "      <td>0.0274</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>1789</td>\n",
       "      <td>6893</td>\n",
       "      <td>0.2595</td>\n",
       "      <td>95</td>\n",
       "      <td>1695</td>\n",
       "      <td>515</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>53.70</td>\n",
       "      <td>24.19</td>\n",
       "      <td>29.51</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>1495</td>\n",
       "      <td>6953</td>\n",
       "      <td>0.2150</td>\n",
       "      <td>57</td>\n",
       "      <td>1440</td>\n",
       "      <td>349</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>57.88</td>\n",
       "      <td>29.20</td>\n",
       "      <td>28.68</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0387</td>\n",
       "      <td>0.0387</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-02</th>\n",
       "      <td>1383</td>\n",
       "      <td>7012</td>\n",
       "      <td>0.1972</td>\n",
       "      <td>58</td>\n",
       "      <td>1326</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>58.48</td>\n",
       "      <td>27.69</td>\n",
       "      <td>30.79</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0423</td>\n",
       "      <td>0.0423</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-03</th>\n",
       "      <td>1214</td>\n",
       "      <td>7054</td>\n",
       "      <td>0.1721</td>\n",
       "      <td>39</td>\n",
       "      <td>1176</td>\n",
       "      <td>207</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>52.40</td>\n",
       "      <td>27.26</td>\n",
       "      <td>25.14</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0432</td>\n",
       "      <td>0.0432</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-04</th>\n",
       "      <td>1126</td>\n",
       "      <td>7087</td>\n",
       "      <td>0.1589</td>\n",
       "      <td>25</td>\n",
       "      <td>1101</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>40.32</td>\n",
       "      <td>22.36</td>\n",
       "      <td>17.96</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-05</th>\n",
       "      <td>1043</td>\n",
       "      <td>7112</td>\n",
       "      <td>0.1467</td>\n",
       "      <td>26</td>\n",
       "      <td>1018</td>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.80</td>\n",
       "      <td>20.06</td>\n",
       "      <td>17.74</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-06</th>\n",
       "      <td>941</td>\n",
       "      <td>7136</td>\n",
       "      <td>0.1319</td>\n",
       "      <td>20</td>\n",
       "      <td>921</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>35.86</td>\n",
       "      <td>16.32</td>\n",
       "      <td>19.54</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0381</td>\n",
       "      <td>0.0381</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-07</th>\n",
       "      <td>920</td>\n",
       "      <td>7160</td>\n",
       "      <td>0.1285</td>\n",
       "      <td>19</td>\n",
       "      <td>901</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>27.61</td>\n",
       "      <td>16.07</td>\n",
       "      <td>11.54</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-08</th>\n",
       "      <td>846</td>\n",
       "      <td>7182</td>\n",
       "      <td>0.1178</td>\n",
       "      <td>25</td>\n",
       "      <td>822</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>35.46</td>\n",
       "      <td>14.14</td>\n",
       "      <td>21.32</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0419</td>\n",
       "      <td>0.0419</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-09</th>\n",
       "      <td>772</td>\n",
       "      <td>7196</td>\n",
       "      <td>0.1073</td>\n",
       "      <td>20</td>\n",
       "      <td>754</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19.61</td>\n",
       "      <td>9.24</td>\n",
       "      <td>10.37</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0254</td>\n",
       "      <td>0.0254</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-10</th>\n",
       "      <td>738</td>\n",
       "      <td>7212</td>\n",
       "      <td>0.1023</td>\n",
       "      <td>17</td>\n",
       "      <td>721</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26.23</td>\n",
       "      <td>12.25</td>\n",
       "      <td>13.98</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0355</td>\n",
       "      <td>0.0355</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-11</th>\n",
       "      <td>810</td>\n",
       "      <td>7227</td>\n",
       "      <td>0.1121</td>\n",
       "      <td>14</td>\n",
       "      <td>796</td>\n",
       "      <td>-58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21.49</td>\n",
       "      <td>9.35</td>\n",
       "      <td>12.14</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0265</td>\n",
       "      <td>0.0265</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-12</th>\n",
       "      <td>725</td>\n",
       "      <td>7242</td>\n",
       "      <td>0.1001</td>\n",
       "      <td>15</td>\n",
       "      <td>711</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.84</td>\n",
       "      <td>9.85</td>\n",
       "      <td>13.99</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0329</td>\n",
       "      <td>0.0329</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-13</th>\n",
       "      <td>695</td>\n",
       "      <td>7253</td>\n",
       "      <td>0.0958</td>\n",
       "      <td>13</td>\n",
       "      <td>685</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26.77</td>\n",
       "      <td>14.46</td>\n",
       "      <td>12.31</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-14</th>\n",
       "      <td>6183</td>\n",
       "      <td>12761</td>\n",
       "      <td>0.4845</td>\n",
       "      <td>5481</td>\n",
       "      <td>720</td>\n",
       "      <td>-25</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>28.95</td>\n",
       "      <td>493.01</td>\n",
       "      <td>278.69</td>\n",
       "      <td>214.32</td>\n",
       "      <td>0.9445</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>0.0797</td>\n",
       "      <td>0.0844</td>\n",
       "      <td>7.2375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-15</th>\n",
       "      <td>12217</td>\n",
       "      <td>21519</td>\n",
       "      <td>0.5677</td>\n",
       "      <td>8691</td>\n",
       "      <td>3548</td>\n",
       "      <td>2635</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>58.83</td>\n",
       "      <td>1030.41</td>\n",
       "      <td>587.68</td>\n",
       "      <td>442.73</td>\n",
       "      <td>0.9460</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0843</td>\n",
       "      <td>0.0892</td>\n",
       "      <td>4.5254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-16</th>\n",
       "      <td>15363</td>\n",
       "      <td>29692</td>\n",
       "      <td>0.5174</td>\n",
       "      <td>8119</td>\n",
       "      <td>7266</td>\n",
       "      <td>4951</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>45.88</td>\n",
       "      <td>1451.98</td>\n",
       "      <td>855.96</td>\n",
       "      <td>596.02</td>\n",
       "      <td>0.9694</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.0945</td>\n",
       "      <td>0.0975</td>\n",
       "      <td>4.1709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-17</th>\n",
       "      <td>18320</td>\n",
       "      <td>38164</td>\n",
       "      <td>0.4800</td>\n",
       "      <td>8412</td>\n",
       "      <td>9934</td>\n",
       "      <td>5429</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>114.75</td>\n",
       "      <td>1829.71</td>\n",
       "      <td>1086.71</td>\n",
       "      <td>743.00</td>\n",
       "      <td>0.9410</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>0.1061</td>\n",
       "      <td>6.3750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-18</th>\n",
       "      <td>22728</td>\n",
       "      <td>48737</td>\n",
       "      <td>0.4663</td>\n",
       "      <td>10461</td>\n",
       "      <td>12294</td>\n",
       "      <td>6026</td>\n",
       "      <td>24</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>109.69</td>\n",
       "      <td>2444.21</td>\n",
       "      <td>1479.50</td>\n",
       "      <td>964.71</td>\n",
       "      <td>0.9571</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.1075</td>\n",
       "      <td>0.1124</td>\n",
       "      <td>4.5704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-19</th>\n",
       "      <td>26864</td>\n",
       "      <td>59949</td>\n",
       "      <td>0.4481</td>\n",
       "      <td>11094</td>\n",
       "      <td>15810</td>\n",
       "      <td>6918</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>87.78</td>\n",
       "      <td>2642.59</td>\n",
       "      <td>1689.18</td>\n",
       "      <td>953.41</td>\n",
       "      <td>0.9679</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.0984</td>\n",
       "      <td>0.1016</td>\n",
       "      <td>4.3890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-20</th>\n",
       "      <td>29920</td>\n",
       "      <td>71169</td>\n",
       "      <td>0.4204</td>\n",
       "      <td>11149</td>\n",
       "      <td>18827</td>\n",
       "      <td>8037</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>144.76</td>\n",
       "      <td>2833.31</td>\n",
       "      <td>1818.63</td>\n",
       "      <td>1014.68</td>\n",
       "      <td>0.9514</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0947</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>7.2380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-21</th>\n",
       "      <td>32718</td>\n",
       "      <td>82582</td>\n",
       "      <td>0.3962</td>\n",
       "      <td>11229</td>\n",
       "      <td>21542</td>\n",
       "      <td>8378</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>119.68</td>\n",
       "      <td>2669.90</td>\n",
       "      <td>1399.98</td>\n",
       "      <td>1269.92</td>\n",
       "      <td>0.9571</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0037</td>\n",
       "      <td>0.0816</td>\n",
       "      <td>0.0853</td>\n",
       "      <td>4.4326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-22</th>\n",
       "      <td>37314</td>\n",
       "      <td>96049</td>\n",
       "      <td>0.3885</td>\n",
       "      <td>13397</td>\n",
       "      <td>23974</td>\n",
       "      <td>8744</td>\n",
       "      <td>28</td>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>174.62</td>\n",
       "      <td>3182.25</td>\n",
       "      <td>1627.08</td>\n",
       "      <td>1555.17</td>\n",
       "      <td>0.9480</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>0.0853</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>6.2364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-23</th>\n",
       "      <td>43041</td>\n",
       "      <td>111701</td>\n",
       "      <td>0.3853</td>\n",
       "      <td>15799</td>\n",
       "      <td>27298</td>\n",
       "      <td>10016</td>\n",
       "      <td>34</td>\n",
       "      <td>47</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>202.53</td>\n",
       "      <td>3457.10</td>\n",
       "      <td>1703.23</td>\n",
       "      <td>1753.87</td>\n",
       "      <td>0.9447</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>0.0803</td>\n",
       "      <td>0.0850</td>\n",
       "      <td>5.9568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-24</th>\n",
       "      <td>49339</td>\n",
       "      <td>129107</td>\n",
       "      <td>0.3822</td>\n",
       "      <td>17638</td>\n",
       "      <td>31777</td>\n",
       "      <td>11264</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>241.65</td>\n",
       "      <td>3874.87</td>\n",
       "      <td>1946.20</td>\n",
       "      <td>1928.67</td>\n",
       "      <td>0.9413</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0785</td>\n",
       "      <td>0.0834</td>\n",
       "      <td>8.9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-25</th>\n",
       "      <td>51121</td>\n",
       "      <td>144377</td>\n",
       "      <td>0.3541</td>\n",
       "      <td>15538</td>\n",
       "      <td>35669</td>\n",
       "      <td>13670</td>\n",
       "      <td>31</td>\n",
       "      <td>42</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>259.58</td>\n",
       "      <td>4040.29</td>\n",
       "      <td>1839.50</td>\n",
       "      <td>2200.79</td>\n",
       "      <td>0.9396</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.0790</td>\n",
       "      <td>0.0841</td>\n",
       "      <td>8.3735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              DAU     MAU    用户黏度    DNU    DOU    DCU  DPU  付费次数  首次付费用户数  \\\n",
       "日期                                                                           \n",
       "2019-02-19     14      30  0.4667     10      6     -1    0     0        0   \n",
       "2019-02-20    164     190  0.8632    156      8      6    0     0        0   \n",
       "2019-02-21    796     911  0.8738    708     93     71    0     0        0   \n",
       "2019-02-22   1341    1930  0.6948    987    367    429    1     1        1   \n",
       "2019-02-23   1566    2901  0.5398    931    642    699    0     0        0   \n",
       "2019-02-24   1970    4019  0.4902   1100    876    690    3     4        3   \n",
       "2019-02-25   2292    5111  0.4484   1054   1243    727    1     1        1   \n",
       "2019-02-26   2583    6251  0.4132   1125   1467    825    0     0        0   \n",
       "2019-02-27   2210    6779  0.3260    488   1724    859    0     0        0   \n",
       "2019-02-28   1789    6893  0.2595     95   1695    515    0     0        0   \n",
       "2019-03-01   1495    6953  0.2150     57   1440    349    0     0        0   \n",
       "2019-03-02   1383    7012  0.1972     58   1326    169    0     0        0   \n",
       "2019-03-03   1214    7054  0.1721     39   1176    207    0     0        0   \n",
       "2019-03-04   1126    7087  0.1589     25   1101    113    0     0        0   \n",
       "2019-03-05   1043    7112  0.1467     26   1018    108    0     0        0   \n",
       "2019-03-06    941    7136  0.1319     20    921    122    0     0        0   \n",
       "2019-03-07    920    7160  0.1285     19    901     40    0     0        0   \n",
       "2019-03-08    846    7182  0.1178     25    822     98    0     0        0   \n",
       "2019-03-09    772    7196  0.1073     20    754     92    0     0        0   \n",
       "2019-03-10    738    7212  0.1023     17    721     51    0     0        0   \n",
       "2019-03-11    810    7227  0.1121     14    796    -58    0     0        0   \n",
       "2019-03-12    725    7242  0.1001     15    711     99    0     0        0   \n",
       "2019-03-13    695    7253  0.0958     13    685     40    0     0        0   \n",
       "2019-03-14   6183   12761  0.4845   5481    720    -25    4     5        4   \n",
       "2019-03-15  12217   21519  0.5677   8691   3548   2635   13    17       11   \n",
       "2019-03-16  15363   29692  0.5174   8119   7266   4951   11    12        9   \n",
       "2019-03-17  18320   38164  0.4800   8412   9934   5429   18    25       14   \n",
       "2019-03-18  22728   48737  0.4663  10461  12294   6026   24    31       17   \n",
       "2019-03-19  26864   59949  0.4481  11094  15810   6918   20    22       15   \n",
       "2019-03-20  29920   71169  0.4204  11149  18827   8037   20    24       11   \n",
       "2019-03-21  32718   82582  0.3962  11229  21542   8378   27    32       21   \n",
       "2019-03-22  37314   96049  0.3885  13397  23974   8744   28    38       15   \n",
       "2019-03-23  43041  111701  0.3853  15799  27298  10016   34    47       26   \n",
       "2019-03-24  49339  129107  0.3822  17638  31777  11264   27    35       18   \n",
       "2019-03-25  51121  144377  0.3541  15538  35669  13670   31    42       21   \n",
       "\n",
       "            新增付费用户数    ...     官方流水($)  广告收入($)  插屏广告收入($)  激励视频收入($)  \\\n",
       "日期                     ...                                              \n",
       "2019-02-19        0    ...        0.00     0.00       0.00       0.00   \n",
       "2019-02-20        0    ...        0.00     2.38       0.09       2.29   \n",
       "2019-02-21        0    ...        0.00     6.25       0.59       5.66   \n",
       "2019-02-22        1    ...        1.99    16.75       7.99       8.76   \n",
       "2019-02-23        0    ...        0.00    41.66      19.01      22.65   \n",
       "2019-02-24        1    ...        6.96    61.63      24.57      37.06   \n",
       "2019-02-25        1    ...        1.99    49.38      31.08      18.30   \n",
       "2019-02-26        0    ...        0.00    67.08      32.50      34.58   \n",
       "2019-02-27        0    ...        0.00    60.56      33.89      26.67   \n",
       "2019-02-28        0    ...        0.00    53.70      24.19      29.51   \n",
       "2019-03-01        0    ...        0.00    57.88      29.20      28.68   \n",
       "2019-03-02        0    ...        0.00    58.48      27.69      30.79   \n",
       "2019-03-03        0    ...        0.00    52.40      27.26      25.14   \n",
       "2019-03-04        0    ...        0.00    40.32      22.36      17.96   \n",
       "2019-03-05        0    ...        0.00    37.80      20.06      17.74   \n",
       "2019-03-06        0    ...        0.00    35.86      16.32      19.54   \n",
       "2019-03-07        0    ...        0.00    27.61      16.07      11.54   \n",
       "2019-03-08        0    ...        0.00    35.46      14.14      21.32   \n",
       "2019-03-09        0    ...        0.00    19.61       9.24      10.37   \n",
       "2019-03-10        0    ...        0.00    26.23      12.25      13.98   \n",
       "2019-03-11        0    ...        0.00    21.49       9.35      12.14   \n",
       "2019-03-12        0    ...        0.00    23.84       9.85      13.99   \n",
       "2019-03-13        0    ...        0.00    26.77      14.46      12.31   \n",
       "2019-03-14        4    ...       28.95   493.01     278.69     214.32   \n",
       "2019-03-15        6    ...       58.83  1030.41     587.68     442.73   \n",
       "2019-03-16        1    ...       45.88  1451.98     855.96     596.02   \n",
       "2019-03-17        3    ...      114.75  1829.71    1086.71     743.00   \n",
       "2019-03-18        6    ...      109.69  2444.21    1479.50     964.71   \n",
       "2019-03-19        4    ...       87.78  2642.59    1689.18     953.41   \n",
       "2019-03-20        4    ...      144.76  2833.31    1818.63    1014.68   \n",
       "2019-03-21        3    ...      119.68  2669.90    1399.98    1269.92   \n",
       "2019-03-22        4    ...      174.62  3182.25    1627.08    1555.17   \n",
       "2019-03-23        4    ...      202.53  3457.10    1703.23    1753.87   \n",
       "2019-03-24        4    ...      241.65  3874.87    1946.20    1928.67   \n",
       "2019-03-25        5    ...      259.58  4040.29    1839.50    2200.79   \n",
       "\n",
       "            广告占比(%)  付费率(%)  内购ARPU($)  广告ARPU($)  总ARPU($)  ARPPU($)  \n",
       "日期                                                                     \n",
       "2019-02-19   0.0000  0.0000     0.0000     0.0000    0.0000    0.0000  \n",
       "2019-02-20   1.0000  0.0000     0.0000     0.0145    0.0145    0.0000  \n",
       "2019-02-21   1.0000  0.0000     0.0000     0.0079    0.0079    0.0000  \n",
       "2019-02-22   0.8938  0.0007     0.0015     0.0125    0.0140    1.9900  \n",
       "2019-02-23   1.0000  0.0000     0.0000     0.0266    0.0266    0.0000  \n",
       "2019-02-24   0.8985  0.0015     0.0035     0.0313    0.0348    2.3200  \n",
       "2019-02-25   0.9613  0.0004     0.0009     0.0215    0.0224    1.9900  \n",
       "2019-02-26   1.0000  0.0000     0.0000     0.0260    0.0260    0.0000  \n",
       "2019-02-27   1.0000  0.0000     0.0000     0.0274    0.0274    0.0000  \n",
       "2019-02-28   1.0000  0.0000     0.0000     0.0300    0.0300    0.0000  \n",
       "2019-03-01   1.0000  0.0000     0.0000     0.0387    0.0387    0.0000  \n",
       "2019-03-02   1.0000  0.0000     0.0000     0.0423    0.0423    0.0000  \n",
       "2019-03-03   1.0000  0.0000     0.0000     0.0432    0.0432    0.0000  \n",
       "2019-03-04   1.0000  0.0000     0.0000     0.0358    0.0358    0.0000  \n",
       "2019-03-05   1.0000  0.0000     0.0000     0.0362    0.0362    0.0000  \n",
       "2019-03-06   1.0000  0.0000     0.0000     0.0381    0.0381    0.0000  \n",
       "2019-03-07   1.0000  0.0000     0.0000     0.0300    0.0300    0.0000  \n",
       "2019-03-08   1.0000  0.0000     0.0000     0.0419    0.0419    0.0000  \n",
       "2019-03-09   1.0000  0.0000     0.0000     0.0254    0.0254    0.0000  \n",
       "2019-03-10   1.0000  0.0000     0.0000     0.0355    0.0355    0.0000  \n",
       "2019-03-11   1.0000  0.0000     0.0000     0.0265    0.0265    0.0000  \n",
       "2019-03-12   1.0000  0.0000     0.0000     0.0329    0.0329    0.0000  \n",
       "2019-03-13   1.0000  0.0000     0.0000     0.0385    0.0385    0.0000  \n",
       "2019-03-14   0.9445  0.0006     0.0047     0.0797    0.0844    7.2375  \n",
       "2019-03-15   0.9460  0.0011     0.0048     0.0843    0.0892    4.5254  \n",
       "2019-03-16   0.9694  0.0007     0.0030     0.0945    0.0975    4.1709  \n",
       "2019-03-17   0.9410  0.0010     0.0063     0.0999    0.1061    6.3750  \n",
       "2019-03-18   0.9571  0.0011     0.0048     0.1075    0.1124    4.5704  \n",
       "2019-03-19   0.9679  0.0007     0.0033     0.0984    0.1016    4.3890  \n",
       "2019-03-20   0.9514  0.0007     0.0048     0.0947    0.0995    7.2380  \n",
       "2019-03-21   0.9571  0.0008     0.0037     0.0816    0.0853    4.4326  \n",
       "2019-03-22   0.9480  0.0008     0.0047     0.0853    0.0900    6.2364  \n",
       "2019-03-23   0.9447  0.0008     0.0047     0.0803    0.0850    5.9568  \n",
       "2019-03-24   0.9413  0.0005     0.0049     0.0785    0.0834    8.9500  \n",
       "2019-03-25   0.9396  0.0006     0.0051     0.0790    0.0841    8.3735  \n",
       "\n",
       "[35 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#指定行索引\n",
    "# index_col 表示用 .xlsx 文件中第几列做行索引\n",
    "df2 = pd.read_excel(r'/Users/mc-016/Desktop/toycrushbrick.xlsx',sheet_name = '收入',index_col = 0)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2019-02-19 00:00:00</th>\n",
       "      <th>14</th>\n",
       "      <th>30</th>\n",
       "      <th>0.4667</th>\n",
       "      <th>10</th>\n",
       "      <th>6</th>\n",
       "      <th>-1</th>\n",
       "      <th>0</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.2</th>\n",
       "      <th>...</th>\n",
       "      <th>0.6</th>\n",
       "      <th>0.7</th>\n",
       "      <th>0.8</th>\n",
       "      <th>0.9</th>\n",
       "      <th>0.10</th>\n",
       "      <th>0.11</th>\n",
       "      <th>0.12</th>\n",
       "      <th>0.13</th>\n",
       "      <th>0.14</th>\n",
       "      <th>0.15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-02-20</td>\n",
       "      <td>164</td>\n",
       "      <td>190</td>\n",
       "      <td>0.8632</td>\n",
       "      <td>156</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.38</td>\n",
       "      <td>0.09</td>\n",
       "      <td>2.29</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-02-21</td>\n",
       "      <td>796</td>\n",
       "      <td>911</td>\n",
       "      <td>0.8738</td>\n",
       "      <td>708</td>\n",
       "      <td>93</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.59</td>\n",
       "      <td>5.66</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-02-22</td>\n",
       "      <td>1341</td>\n",
       "      <td>1930</td>\n",
       "      <td>0.6948</td>\n",
       "      <td>987</td>\n",
       "      <td>367</td>\n",
       "      <td>429</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.99</td>\n",
       "      <td>16.75</td>\n",
       "      <td>7.99</td>\n",
       "      <td>8.76</td>\n",
       "      <td>0.8938</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>1.9900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-02-23</td>\n",
       "      <td>1566</td>\n",
       "      <td>2901</td>\n",
       "      <td>0.5398</td>\n",
       "      <td>931</td>\n",
       "      <td>642</td>\n",
       "      <td>699</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.66</td>\n",
       "      <td>19.01</td>\n",
       "      <td>22.65</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0266</td>\n",
       "      <td>0.0266</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-02-24</td>\n",
       "      <td>1970</td>\n",
       "      <td>4019</td>\n",
       "      <td>0.4902</td>\n",
       "      <td>1100</td>\n",
       "      <td>876</td>\n",
       "      <td>690</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>6.96</td>\n",
       "      <td>61.63</td>\n",
       "      <td>24.57</td>\n",
       "      <td>37.06</td>\n",
       "      <td>0.8985</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.0313</td>\n",
       "      <td>0.0348</td>\n",
       "      <td>2.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-02-25</td>\n",
       "      <td>2292</td>\n",
       "      <td>5111</td>\n",
       "      <td>0.4484</td>\n",
       "      <td>1054</td>\n",
       "      <td>1243</td>\n",
       "      <td>727</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.99</td>\n",
       "      <td>49.38</td>\n",
       "      <td>31.08</td>\n",
       "      <td>18.30</td>\n",
       "      <td>0.9613</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0215</td>\n",
       "      <td>0.0224</td>\n",
       "      <td>1.9900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-02-26</td>\n",
       "      <td>2583</td>\n",
       "      <td>6251</td>\n",
       "      <td>0.4132</td>\n",
       "      <td>1125</td>\n",
       "      <td>1467</td>\n",
       "      <td>825</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>67.08</td>\n",
       "      <td>32.50</td>\n",
       "      <td>34.58</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0260</td>\n",
       "      <td>0.0260</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-02-27</td>\n",
       "      <td>2210</td>\n",
       "      <td>6779</td>\n",
       "      <td>0.3260</td>\n",
       "      <td>488</td>\n",
       "      <td>1724</td>\n",
       "      <td>859</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>60.56</td>\n",
       "      <td>33.89</td>\n",
       "      <td>26.67</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0274</td>\n",
       "      <td>0.0274</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>1789</td>\n",
       "      <td>6893</td>\n",
       "      <td>0.2595</td>\n",
       "      <td>95</td>\n",
       "      <td>1695</td>\n",
       "      <td>515</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>53.70</td>\n",
       "      <td>24.19</td>\n",
       "      <td>29.51</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>1495</td>\n",
       "      <td>6953</td>\n",
       "      <td>0.2150</td>\n",
       "      <td>57</td>\n",
       "      <td>1440</td>\n",
       "      <td>349</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>57.88</td>\n",
       "      <td>29.20</td>\n",
       "      <td>28.68</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0387</td>\n",
       "      <td>0.0387</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-03-02</td>\n",
       "      <td>1383</td>\n",
       "      <td>7012</td>\n",
       "      <td>0.1972</td>\n",
       "      <td>58</td>\n",
       "      <td>1326</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>58.48</td>\n",
       "      <td>27.69</td>\n",
       "      <td>30.79</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0423</td>\n",
       "      <td>0.0423</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-03-03</td>\n",
       "      <td>1214</td>\n",
       "      <td>7054</td>\n",
       "      <td>0.1721</td>\n",
       "      <td>39</td>\n",
       "      <td>1176</td>\n",
       "      <td>207</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>52.40</td>\n",
       "      <td>27.26</td>\n",
       "      <td>25.14</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0432</td>\n",
       "      <td>0.0432</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>1126</td>\n",
       "      <td>7087</td>\n",
       "      <td>0.1589</td>\n",
       "      <td>25</td>\n",
       "      <td>1101</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>40.32</td>\n",
       "      <td>22.36</td>\n",
       "      <td>17.96</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-03-05</td>\n",
       "      <td>1043</td>\n",
       "      <td>7112</td>\n",
       "      <td>0.1467</td>\n",
       "      <td>26</td>\n",
       "      <td>1018</td>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.80</td>\n",
       "      <td>20.06</td>\n",
       "      <td>17.74</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019-03-06</td>\n",
       "      <td>941</td>\n",
       "      <td>7136</td>\n",
       "      <td>0.1319</td>\n",
       "      <td>20</td>\n",
       "      <td>921</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>35.86</td>\n",
       "      <td>16.32</td>\n",
       "      <td>19.54</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0381</td>\n",
       "      <td>0.0381</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-03-07</td>\n",
       "      <td>920</td>\n",
       "      <td>7160</td>\n",
       "      <td>0.1285</td>\n",
       "      <td>19</td>\n",
       "      <td>901</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>27.61</td>\n",
       "      <td>16.07</td>\n",
       "      <td>11.54</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-03-08</td>\n",
       "      <td>846</td>\n",
       "      <td>7182</td>\n",
       "      <td>0.1178</td>\n",
       "      <td>25</td>\n",
       "      <td>822</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>35.46</td>\n",
       "      <td>14.14</td>\n",
       "      <td>21.32</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0419</td>\n",
       "      <td>0.0419</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>772</td>\n",
       "      <td>7196</td>\n",
       "      <td>0.1073</td>\n",
       "      <td>20</td>\n",
       "      <td>754</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19.61</td>\n",
       "      <td>9.24</td>\n",
       "      <td>10.37</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0254</td>\n",
       "      <td>0.0254</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-03-10</td>\n",
       "      <td>738</td>\n",
       "      <td>7212</td>\n",
       "      <td>0.1023</td>\n",
       "      <td>17</td>\n",
       "      <td>721</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26.23</td>\n",
       "      <td>12.25</td>\n",
       "      <td>13.98</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0355</td>\n",
       "      <td>0.0355</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019-03-11</td>\n",
       "      <td>810</td>\n",
       "      <td>7227</td>\n",
       "      <td>0.1121</td>\n",
       "      <td>14</td>\n",
       "      <td>796</td>\n",
       "      <td>-58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21.49</td>\n",
       "      <td>9.35</td>\n",
       "      <td>12.14</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0265</td>\n",
       "      <td>0.0265</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019-03-12</td>\n",
       "      <td>725</td>\n",
       "      <td>7242</td>\n",
       "      <td>0.1001</td>\n",
       "      <td>15</td>\n",
       "      <td>711</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.84</td>\n",
       "      <td>9.85</td>\n",
       "      <td>13.99</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0329</td>\n",
       "      <td>0.0329</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019-03-13</td>\n",
       "      <td>695</td>\n",
       "      <td>7253</td>\n",
       "      <td>0.0958</td>\n",
       "      <td>13</td>\n",
       "      <td>685</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26.77</td>\n",
       "      <td>14.46</td>\n",
       "      <td>12.31</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019-03-14</td>\n",
       "      <td>6183</td>\n",
       "      <td>12761</td>\n",
       "      <td>0.4845</td>\n",
       "      <td>5481</td>\n",
       "      <td>720</td>\n",
       "      <td>-25</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>28.95</td>\n",
       "      <td>493.01</td>\n",
       "      <td>278.69</td>\n",
       "      <td>214.32</td>\n",
       "      <td>0.9445</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>0.0797</td>\n",
       "      <td>0.0844</td>\n",
       "      <td>7.2375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>12217</td>\n",
       "      <td>21519</td>\n",
       "      <td>0.5677</td>\n",
       "      <td>8691</td>\n",
       "      <td>3548</td>\n",
       "      <td>2635</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>58.83</td>\n",
       "      <td>1030.41</td>\n",
       "      <td>587.68</td>\n",
       "      <td>442.73</td>\n",
       "      <td>0.9460</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0843</td>\n",
       "      <td>0.0892</td>\n",
       "      <td>4.5254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-03-16</td>\n",
       "      <td>15363</td>\n",
       "      <td>29692</td>\n",
       "      <td>0.5174</td>\n",
       "      <td>8119</td>\n",
       "      <td>7266</td>\n",
       "      <td>4951</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>45.88</td>\n",
       "      <td>1451.98</td>\n",
       "      <td>855.96</td>\n",
       "      <td>596.02</td>\n",
       "      <td>0.9694</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.0945</td>\n",
       "      <td>0.0975</td>\n",
       "      <td>4.1709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-03-17</td>\n",
       "      <td>18320</td>\n",
       "      <td>38164</td>\n",
       "      <td>0.4800</td>\n",
       "      <td>8412</td>\n",
       "      <td>9934</td>\n",
       "      <td>5429</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>114.75</td>\n",
       "      <td>1829.71</td>\n",
       "      <td>1086.71</td>\n",
       "      <td>743.00</td>\n",
       "      <td>0.9410</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>0.1061</td>\n",
       "      <td>6.3750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-03-18</td>\n",
       "      <td>22728</td>\n",
       "      <td>48737</td>\n",
       "      <td>0.4663</td>\n",
       "      <td>10461</td>\n",
       "      <td>12294</td>\n",
       "      <td>6026</td>\n",
       "      <td>24</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>109.69</td>\n",
       "      <td>2444.21</td>\n",
       "      <td>1479.50</td>\n",
       "      <td>964.71</td>\n",
       "      <td>0.9571</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.1075</td>\n",
       "      <td>0.1124</td>\n",
       "      <td>4.5704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-03-19</td>\n",
       "      <td>26864</td>\n",
       "      <td>59949</td>\n",
       "      <td>0.4481</td>\n",
       "      <td>11094</td>\n",
       "      <td>15810</td>\n",
       "      <td>6918</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>87.78</td>\n",
       "      <td>2642.59</td>\n",
       "      <td>1689.18</td>\n",
       "      <td>953.41</td>\n",
       "      <td>0.9679</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.0984</td>\n",
       "      <td>0.1016</td>\n",
       "      <td>4.3890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-03-20</td>\n",
       "      <td>29920</td>\n",
       "      <td>71169</td>\n",
       "      <td>0.4204</td>\n",
       "      <td>11149</td>\n",
       "      <td>18827</td>\n",
       "      <td>8037</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>144.76</td>\n",
       "      <td>2833.31</td>\n",
       "      <td>1818.63</td>\n",
       "      <td>1014.68</td>\n",
       "      <td>0.9514</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0947</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>7.2380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-03-21</td>\n",
       "      <td>32718</td>\n",
       "      <td>82582</td>\n",
       "      <td>0.3962</td>\n",
       "      <td>11229</td>\n",
       "      <td>21542</td>\n",
       "      <td>8378</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>119.68</td>\n",
       "      <td>2669.90</td>\n",
       "      <td>1399.98</td>\n",
       "      <td>1269.92</td>\n",
       "      <td>0.9571</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0037</td>\n",
       "      <td>0.0816</td>\n",
       "      <td>0.0853</td>\n",
       "      <td>4.4326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>37314</td>\n",
       "      <td>96049</td>\n",
       "      <td>0.3885</td>\n",
       "      <td>13397</td>\n",
       "      <td>23974</td>\n",
       "      <td>8744</td>\n",
       "      <td>28</td>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>174.62</td>\n",
       "      <td>3182.25</td>\n",
       "      <td>1627.08</td>\n",
       "      <td>1555.17</td>\n",
       "      <td>0.9480</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>0.0853</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>6.2364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019-03-23</td>\n",
       "      <td>43041</td>\n",
       "      <td>111701</td>\n",
       "      <td>0.3853</td>\n",
       "      <td>15799</td>\n",
       "      <td>27298</td>\n",
       "      <td>10016</td>\n",
       "      <td>34</td>\n",
       "      <td>47</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>202.53</td>\n",
       "      <td>3457.10</td>\n",
       "      <td>1703.23</td>\n",
       "      <td>1753.87</td>\n",
       "      <td>0.9447</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>0.0803</td>\n",
       "      <td>0.0850</td>\n",
       "      <td>5.9568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019-03-24</td>\n",
       "      <td>49339</td>\n",
       "      <td>129107</td>\n",
       "      <td>0.3822</td>\n",
       "      <td>17638</td>\n",
       "      <td>31777</td>\n",
       "      <td>11264</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>241.65</td>\n",
       "      <td>3874.87</td>\n",
       "      <td>1946.20</td>\n",
       "      <td>1928.67</td>\n",
       "      <td>0.9413</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0785</td>\n",
       "      <td>0.0834</td>\n",
       "      <td>8.9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2019-03-25</td>\n",
       "      <td>51121</td>\n",
       "      <td>144377</td>\n",
       "      <td>0.3541</td>\n",
       "      <td>15538</td>\n",
       "      <td>35669</td>\n",
       "      <td>13670</td>\n",
       "      <td>31</td>\n",
       "      <td>42</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>259.58</td>\n",
       "      <td>4040.29</td>\n",
       "      <td>1839.50</td>\n",
       "      <td>2200.79</td>\n",
       "      <td>0.9396</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.0790</td>\n",
       "      <td>0.0841</td>\n",
       "      <td>8.3735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   2019-02-19 00:00:00     14      30  0.4667     10      6     -1   0  0.1  \\\n",
       "0           2019-02-20    164     190  0.8632    156      8      6   0    0   \n",
       "1           2019-02-21    796     911  0.8738    708     93     71   0    0   \n",
       "2           2019-02-22   1341    1930  0.6948    987    367    429   1    1   \n",
       "3           2019-02-23   1566    2901  0.5398    931    642    699   0    0   \n",
       "4           2019-02-24   1970    4019  0.4902   1100    876    690   3    4   \n",
       "5           2019-02-25   2292    5111  0.4484   1054   1243    727   1    1   \n",
       "6           2019-02-26   2583    6251  0.4132   1125   1467    825   0    0   \n",
       "7           2019-02-27   2210    6779  0.3260    488   1724    859   0    0   \n",
       "8           2019-02-28   1789    6893  0.2595     95   1695    515   0    0   \n",
       "9           2019-03-01   1495    6953  0.2150     57   1440    349   0    0   \n",
       "10          2019-03-02   1383    7012  0.1972     58   1326    169   0    0   \n",
       "11          2019-03-03   1214    7054  0.1721     39   1176    207   0    0   \n",
       "12          2019-03-04   1126    7087  0.1589     25   1101    113   0    0   \n",
       "13          2019-03-05   1043    7112  0.1467     26   1018    108   0    0   \n",
       "14          2019-03-06    941    7136  0.1319     20    921    122   0    0   \n",
       "15          2019-03-07    920    7160  0.1285     19    901     40   0    0   \n",
       "16          2019-03-08    846    7182  0.1178     25    822     98   0    0   \n",
       "17          2019-03-09    772    7196  0.1073     20    754     92   0    0   \n",
       "18          2019-03-10    738    7212  0.1023     17    721     51   0    0   \n",
       "19          2019-03-11    810    7227  0.1121     14    796    -58   0    0   \n",
       "20          2019-03-12    725    7242  0.1001     15    711     99   0    0   \n",
       "21          2019-03-13    695    7253  0.0958     13    685     40   0    0   \n",
       "22          2019-03-14   6183   12761  0.4845   5481    720    -25   4    5   \n",
       "23          2019-03-15  12217   21519  0.5677   8691   3548   2635  13   17   \n",
       "24          2019-03-16  15363   29692  0.5174   8119   7266   4951  11   12   \n",
       "25          2019-03-17  18320   38164  0.4800   8412   9934   5429  18   25   \n",
       "26          2019-03-18  22728   48737  0.4663  10461  12294   6026  24   31   \n",
       "27          2019-03-19  26864   59949  0.4481  11094  15810   6918  20   22   \n",
       "28          2019-03-20  29920   71169  0.4204  11149  18827   8037  20   24   \n",
       "29          2019-03-21  32718   82582  0.3962  11229  21542   8378  27   32   \n",
       "30          2019-03-22  37314   96049  0.3885  13397  23974   8744  28   38   \n",
       "31          2019-03-23  43041  111701  0.3853  15799  27298  10016  34   47   \n",
       "32          2019-03-24  49339  129107  0.3822  17638  31777  11264  27   35   \n",
       "33          2019-03-25  51121  144377  0.3541  15538  35669  13670  31   42   \n",
       "\n",
       "    0.2   ...       0.6      0.7      0.8      0.9    0.10    0.11    0.12  \\\n",
       "0     0   ...      0.00     2.38     0.09     2.29  1.0000  0.0000  0.0000   \n",
       "1     0   ...      0.00     6.25     0.59     5.66  1.0000  0.0000  0.0000   \n",
       "2     1   ...      1.99    16.75     7.99     8.76  0.8938  0.0007  0.0015   \n",
       "3     0   ...      0.00    41.66    19.01    22.65  1.0000  0.0000  0.0000   \n",
       "4     3   ...      6.96    61.63    24.57    37.06  0.8985  0.0015  0.0035   \n",
       "5     1   ...      1.99    49.38    31.08    18.30  0.9613  0.0004  0.0009   \n",
       "6     0   ...      0.00    67.08    32.50    34.58  1.0000  0.0000  0.0000   \n",
       "7     0   ...      0.00    60.56    33.89    26.67  1.0000  0.0000  0.0000   \n",
       "8     0   ...      0.00    53.70    24.19    29.51  1.0000  0.0000  0.0000   \n",
       "9     0   ...      0.00    57.88    29.20    28.68  1.0000  0.0000  0.0000   \n",
       "10    0   ...      0.00    58.48    27.69    30.79  1.0000  0.0000  0.0000   \n",
       "11    0   ...      0.00    52.40    27.26    25.14  1.0000  0.0000  0.0000   \n",
       "12    0   ...      0.00    40.32    22.36    17.96  1.0000  0.0000  0.0000   \n",
       "13    0   ...      0.00    37.80    20.06    17.74  1.0000  0.0000  0.0000   \n",
       "14    0   ...      0.00    35.86    16.32    19.54  1.0000  0.0000  0.0000   \n",
       "15    0   ...      0.00    27.61    16.07    11.54  1.0000  0.0000  0.0000   \n",
       "16    0   ...      0.00    35.46    14.14    21.32  1.0000  0.0000  0.0000   \n",
       "17    0   ...      0.00    19.61     9.24    10.37  1.0000  0.0000  0.0000   \n",
       "18    0   ...      0.00    26.23    12.25    13.98  1.0000  0.0000  0.0000   \n",
       "19    0   ...      0.00    21.49     9.35    12.14  1.0000  0.0000  0.0000   \n",
       "20    0   ...      0.00    23.84     9.85    13.99  1.0000  0.0000  0.0000   \n",
       "21    0   ...      0.00    26.77    14.46    12.31  1.0000  0.0000  0.0000   \n",
       "22    4   ...     28.95   493.01   278.69   214.32  0.9445  0.0006  0.0047   \n",
       "23   11   ...     58.83  1030.41   587.68   442.73  0.9460  0.0011  0.0048   \n",
       "24    9   ...     45.88  1451.98   855.96   596.02  0.9694  0.0007  0.0030   \n",
       "25   14   ...    114.75  1829.71  1086.71   743.00  0.9410  0.0010  0.0063   \n",
       "26   17   ...    109.69  2444.21  1479.50   964.71  0.9571  0.0011  0.0048   \n",
       "27   15   ...     87.78  2642.59  1689.18   953.41  0.9679  0.0007  0.0033   \n",
       "28   11   ...    144.76  2833.31  1818.63  1014.68  0.9514  0.0007  0.0048   \n",
       "29   21   ...    119.68  2669.90  1399.98  1269.92  0.9571  0.0008  0.0037   \n",
       "30   15   ...    174.62  3182.25  1627.08  1555.17  0.9480  0.0008  0.0047   \n",
       "31   26   ...    202.53  3457.10  1703.23  1753.87  0.9447  0.0008  0.0047   \n",
       "32   18   ...    241.65  3874.87  1946.20  1928.67  0.9413  0.0005  0.0049   \n",
       "33   21   ...    259.58  4040.29  1839.50  2200.79  0.9396  0.0006  0.0051   \n",
       "\n",
       "      0.13    0.14    0.15  \n",
       "0   0.0145  0.0145  0.0000  \n",
       "1   0.0079  0.0079  0.0000  \n",
       "2   0.0125  0.0140  1.9900  \n",
       "3   0.0266  0.0266  0.0000  \n",
       "4   0.0313  0.0348  2.3200  \n",
       "5   0.0215  0.0224  1.9900  \n",
       "6   0.0260  0.0260  0.0000  \n",
       "7   0.0274  0.0274  0.0000  \n",
       "8   0.0300  0.0300  0.0000  \n",
       "9   0.0387  0.0387  0.0000  \n",
       "10  0.0423  0.0423  0.0000  \n",
       "11  0.0432  0.0432  0.0000  \n",
       "12  0.0358  0.0358  0.0000  \n",
       "13  0.0362  0.0362  0.0000  \n",
       "14  0.0381  0.0381  0.0000  \n",
       "15  0.0300  0.0300  0.0000  \n",
       "16  0.0419  0.0419  0.0000  \n",
       "17  0.0254  0.0254  0.0000  \n",
       "18  0.0355  0.0355  0.0000  \n",
       "19  0.0265  0.0265  0.0000  \n",
       "20  0.0329  0.0329  0.0000  \n",
       "21  0.0385  0.0385  0.0000  \n",
       "22  0.0797  0.0844  7.2375  \n",
       "23  0.0843  0.0892  4.5254  \n",
       "24  0.0945  0.0975  4.1709  \n",
       "25  0.0999  0.1061  6.3750  \n",
       "26  0.1075  0.1124  4.5704  \n",
       "27  0.0984  0.1016  4.3890  \n",
       "28  0.0947  0.0995  7.2380  \n",
       "29  0.0816  0.0853  4.4326  \n",
       "30  0.0853  0.0900  6.2364  \n",
       "31  0.0803  0.0850  5.9568  \n",
       "32  0.0785  0.0834  8.9500  \n",
       "33  0.0790  0.0841  8.3735  \n",
       "\n",
       "[34 rows x 23 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 指定列索引\n",
    "df = pd.read_excel(r'/Users/mc-016/Desktop/toycrushbrick.xlsx',sheet_name = '收入',header = 1) #指定第一行做索引\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>DAU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>12217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>15363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>18320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>22728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>26864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>32718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>37314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>43041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>49339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>51121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DAU\n",
       "0      14\n",
       "1     164\n",
       "2     796\n",
       "3    1341\n",
       "4    1566\n",
       "5    1970\n",
       "6    2292\n",
       "7    2583\n",
       "8    2210\n",
       "9    1789\n",
       "10   1495\n",
       "11   1383\n",
       "12   1214\n",
       "13   1126\n",
       "14   1043\n",
       "15    941\n",
       "16    920\n",
       "17    846\n",
       "18    772\n",
       "19    738\n",
       "20    810\n",
       "21    725\n",
       "22    695\n",
       "23   6183\n",
       "24  12217\n",
       "25  15363\n",
       "26  18320\n",
       "27  22728\n",
       "28  26864\n",
       "29  29920\n",
       "30  32718\n",
       "31  37314\n",
       "32  43041\n",
       "33  49339\n",
       "34  51121"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入指定列\n",
    "import pandas as pd\n",
    "df = pd.read_excel(r'/Users/mc-016/Desktop/toycrushbrick.xlsx',sheet_name = '收入',usecols = [1])\n",
    "# usecols做切片导入指定列\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>日期</th>\n",
       "      <th>MAU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-02-19</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-02-20</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-02-21</td>\n",
       "      <td>911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-02-22</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-02-23</td>\n",
       "      <td>2901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-02-24</td>\n",
       "      <td>4019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-02-25</td>\n",
       "      <td>5111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-02-26</td>\n",
       "      <td>6251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-02-27</td>\n",
       "      <td>6779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>6893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>6953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-03-02</td>\n",
       "      <td>7012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-03-03</td>\n",
       "      <td>7054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>7087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019-03-05</td>\n",
       "      <td>7112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-03-06</td>\n",
       "      <td>7136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-03-07</td>\n",
       "      <td>7160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-03-08</td>\n",
       "      <td>7182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-03-09</td>\n",
       "      <td>7196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019-03-10</td>\n",
       "      <td>7212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019-03-11</td>\n",
       "      <td>7227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019-03-12</td>\n",
       "      <td>7242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019-03-13</td>\n",
       "      <td>7253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019-03-14</td>\n",
       "      <td>12761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-03-15</td>\n",
       "      <td>21519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-03-16</td>\n",
       "      <td>29692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-03-17</td>\n",
       "      <td>38164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-03-18</td>\n",
       "      <td>48737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-03-19</td>\n",
       "      <td>59949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-03-20</td>\n",
       "      <td>71169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019-03-21</td>\n",
       "      <td>82582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>96049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019-03-23</td>\n",
       "      <td>111701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2019-03-24</td>\n",
       "      <td>129107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2019-03-25</td>\n",
       "      <td>144377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           日期     MAU\n",
       "0  2019-02-19      30\n",
       "1  2019-02-20     190\n",
       "2  2019-02-21     911\n",
       "3  2019-02-22    1930\n",
       "4  2019-02-23    2901\n",
       "5  2019-02-24    4019\n",
       "6  2019-02-25    5111\n",
       "7  2019-02-26    6251\n",
       "8  2019-02-27    6779\n",
       "9  2019-02-28    6893\n",
       "10 2019-03-01    6953\n",
       "11 2019-03-02    7012\n",
       "12 2019-03-03    7054\n",
       "13 2019-03-04    7087\n",
       "14 2019-03-05    7112\n",
       "15 2019-03-06    7136\n",
       "16 2019-03-07    7160\n",
       "17 2019-03-08    7182\n",
       "18 2019-03-09    7196\n",
       "19 2019-03-10    7212\n",
       "20 2019-03-11    7227\n",
       "21 2019-03-12    7242\n",
       "22 2019-03-13    7253\n",
       "23 2019-03-14   12761\n",
       "24 2019-03-15   21519\n",
       "25 2019-03-16   29692\n",
       "26 2019-03-17   38164\n",
       "27 2019-03-18   48737\n",
       "28 2019-03-19   59949\n",
       "29 2019-03-20   71169\n",
       "30 2019-03-21   82582\n",
       "31 2019-03-22   96049\n",
       "32 2019-03-23  111701\n",
       "33 2019-03-24  129107\n",
       "34 2019-03-25  144377"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(r'/Users/mc-016/Desktop/toycrushbrick.xlsx',sheet_name = '收入',usecols = [0,2]) #以列表的形式传入多值\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入 .csv 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>用户ID</th>\n",
       "      <th>客户分类</th>\n",
       "      <th>区域</th>\n",
       "      <th>是否省会</th>\n",
       "      <th>7月销量</th>\n",
       "      <th>8月销量</th>\n",
       "      <th>9月销量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59224</td>\n",
       "      <td>A类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55295</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>37</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46035</td>\n",
       "      <td>A类</td>\n",
       "      <td>二线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2459</td>\n",
       "      <td>C类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22179</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    用户ID 客户分类    区域 是否省会  7月销量  8月销量  9月销量\n",
       "0  59224   A类  一线城市    是     6    20     0\n",
       "1  55295   B类  三线城市    否    37    27    35\n",
       "2  46035   A类  二线城市    是     8     1     8\n",
       "3   2459   C类  一线城市    是     7     8    14\n",
       "4  22179   B类  三线城市    否     9    12     4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 直接导入\n",
    "import pandas as pd \n",
    "df = pd.read_csv(r'/Users/mc-016/Desktop/train-pivot.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>用户ID,客户分类,区域,是否省会,7月销量,8月销量,9月销量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59224,A类,一线城市,是,6,20,0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55295,B类,三线城市,否,37,27,35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46035,A类,二线城市,是,8,1,8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2459,C类,一线城市,是,7,8,14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22179,B类,三线城市,否,9,12,4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  用户ID,客户分类,区域,是否省会,7月销量,8月销量,9月销量\n",
       "0           59224,A类,一线城市,是,6,20,0\n",
       "1         55295,B类,三线城市,否,37,27,35\n",
       "2            46035,A类,二线城市,是,8,1,8\n",
       "3            2459,C类,一线城市,是,7,8,14\n",
       "4           22179,B类,三线城市,否,9,12,4"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 指定分隔符号\n",
    "df = pd.read_csv(r'/Users/mc-016/Desktop/train-pivot.csv',sep = ' ')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户ID</th>\n",
       "      <th>客户分类</th>\n",
       "      <th>区域</th>\n",
       "      <th>是否省会</th>\n",
       "      <th>7月销量</th>\n",
       "      <th>8月销量</th>\n",
       "      <th>9月销量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59224</td>\n",
       "      <td>A类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55295</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>37</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46035</td>\n",
       "      <td>A类</td>\n",
       "      <td>二线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2459</td>\n",
       "      <td>C类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22179</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    用户ID 客户分类    区域 是否省会  7月销量  8月销量  9月销量\n",
       "0  59224   A类  一线城市    是     6    20     0\n",
       "1  55295   B类  三线城市    否    37    27    35\n",
       "2  46035   A类  二线城市    是     8     1     8\n",
       "3   2459   C类  一线城市    是     7     8    14\n",
       "4  22179   B类  三线城市    否     9    12     4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'/Users/mc-016/Desktop/train-pivot.csv',sep = ',')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>用户ID</th>\n",
       "      <th>客户分类</th>\n",
       "      <th>区域</th>\n",
       "      <th>是否省会</th>\n",
       "      <th>7月销量</th>\n",
       "      <th>8月销量</th>\n",
       "      <th>9月销量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59224</td>\n",
       "      <td>A类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55295</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>37</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    用户ID 客户分类    区域 是否省会  7月销量  8月销量  9月销量\n",
       "0  59224   A类  一线城市    是     6    20     0\n",
       "1  55295   B类  三线城市    否    37    27    35"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 指明读取行数\n",
    "df = pd.read_csv(r'/Users/mc-016/Desktop/train-pivot.csv',sep = ',',nrows = 2)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户ID</th>\n",
       "      <th>客户分类</th>\n",
       "      <th>区域</th>\n",
       "      <th>是否省会</th>\n",
       "      <th>7月销量</th>\n",
       "      <th>8月销量</th>\n",
       "      <th>9月销量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59224</td>\n",
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       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55295</td>\n",
       "      <td>B类</td>\n",
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       "      <td>否</td>\n",
       "      <td>37</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46035</td>\n",
       "      <td>A类</td>\n",
       "      <td>二线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2459</td>\n",
       "      <td>C类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22179</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    用户ID 客户分类    区域 是否省会  7月销量  8月销量  9月销量\n",
       "0  59224   A类  一线城市    是     6    20     0\n",
       "1  55295   B类  三线城市    否    37    27    35\n",
       "2  46035   A类  二线城市    是     8     1     8\n",
       "3   2459   C类  一线城市    是     7     8    14\n",
       "4  22179   B类  三线城市    否     9    12     4"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 指定编码格式，python默认编码格式是UTF-8\n",
    "# CSV（逗号分隔）(*.csv)的文件，编码格式gbk\n",
    "df = pd.read_csv(r'/Users/mc-016/Desktop/train-pivot.csv',encoding = 'gbk',sep = ',')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户ID</th>\n",
       "      <th>客户分类</th>\n",
       "      <th>区域</th>\n",
       "      <th>是否省会</th>\n",
       "      <th>7月销量</th>\n",
       "      <th>8月销量</th>\n",
       "      <th>9月销量</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>6</td>\n",
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       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>55295</td>\n",
       "      <td>B类</td>\n",
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       "      <td>否</td>\n",
       "      <td>37</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46035</td>\n",
       "      <td>A类</td>\n",
       "      <td>二线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2459</td>\n",
       "      <td>C类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22179</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    用户ID 客户分类    区域 是否省会  7月销量  8月销量  9月销量\n",
       "0  59224   A类  一线城市    是     6    20     0\n",
       "1  55295   B类  三线城市    否    37    27    35\n",
       "2  46035   A类  二线城市    是     8     1     8\n",
       "3   2459   C类  一线城市    是     7     8    14\n",
       "4  22179   B类  三线城市    否     9    12     4"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# engine 指定\n",
    "df = pd.read_csv(r'/Users/mc-016/Desktop/对比Excel,轻松学习Python数据分析数据集/train-pivot.csv',\n",
    "                 engine = 'python',encoding = 'gbk')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>客户分类</th>\n",
       "      <th>区域</th>\n",
       "      <th>是否省会</th>\n",
       "      <th>7月销量</th>\n",
       "      <th>8月销量</th>\n",
       "      <th>9月销量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59224</td>\n",
       "      <td>A类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55295</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>37</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46035</td>\n",
       "      <td>A类</td>\n",
       "      <td>二线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2459</td>\n",
       "      <td>C类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22179</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    用户ID 客户分类    区域 是否省会  7月销量  8月销量  9月销量\n",
       "0  59224   A类  一线城市    是     6    20     0\n",
       "1  55295   B类  三线城市    否    37    27    35\n",
       "2  46035   A类  二线城市    是     8     1     8\n",
       "3   2459   C类  一线城市    是     7     8    14\n",
       "4  22179   B类  三线城市    否     9    12     4"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'/Users/mc-016/Desktop/对比Excel,轻松学习Python数据分析数据集/train-pivot.csv',encoding = 'gbk')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入.txt文件\n",
    "#### Excel 实现\n",
    "    数据--自文本--导入txt文件，格式选择：UTF-8\n",
    "#### Python 实现\n",
    "    read_table()一定要指定分隔符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>用户ID</th>\n",
       "      <th>客户分类</th>\n",
       "      <th>区域</th>\n",
       "      <th>是否省会</th>\n",
       "      <th>7月销量</th>\n",
       "      <th>8月销量</th>\n",
       "      <th>9月销量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59224</td>\n",
       "      <td>A类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55295</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>37.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46035</td>\n",
       "      <td>A类</td>\n",
       "      <td>二线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2459</td>\n",
       "      <td>C类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22179</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    用户ID 客户分类    区域 是否省会  7月销量  8月销量  9月销量\n",
       "0  59224   A类  一线城市    是   6.0  20.0   0.0\n",
       "1  55295   B类  三线城市    否  37.0  27.0   NaN\n",
       "2  46035   A类  二线城市    是   8.0   1.0   8.0\n",
       "3   2459   C类  一线城市    是   7.0   NaN  14.0\n",
       "4  22179   B类  三线城市    否   NaN  12.0   4.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 利用read_table()导入.txt文件\n",
    "import pandas as pd\n",
    "df1 = pd.read_table(r'/Users/mc-016/Desktop/新建应用2.txt',sep = '\\t',engine='python')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户ID</th>\n",
       "      <th>客户分类</th>\n",
       "      <th>区域</th>\n",
       "      <th>是否省会</th>\n",
       "      <th>7月销量</th>\n",
       "      <th>8月销量</th>\n",
       "      <th>9月销量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59224</td>\n",
       "      <td>A类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55295</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>37</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46035</td>\n",
       "      <td>A类</td>\n",
       "      <td>二线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2459</td>\n",
       "      <td>C类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22179</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    用户ID 客户分类    区域 是否省会  7月销量  8月销量  9月销量\n",
       "0  59224   A类  一线城市    是     6    20     0\n",
       "1  55295   B类  三线城市    否    37    27    35\n",
       "2  46035   A类  二线城市    是     8     1     8\n",
       "3   2459   C类  一线城市    是     7     8    14\n",
       "4  22179   B类  三线城市    否     9    12     4"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 利用read_table()导入.csv文件\n",
    "df2 = pd.read_table(r'/Users/mc-016/Desktop/对比Excel,轻松学习Python数据分析数据集/train-pivot.csv',\n",
    "                    encoding = 'gbk',sep = ',')\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入sql文件 （暂时搁置，后面再学习）\n",
    "#### Excel实现\n",
    "#### Python实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 熟悉数据\n",
    "#### 利用head预览前几行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    DAU\n",
      "0    14\n",
      "1   164\n",
      "2   796\n",
      "3  1341\n",
      "4  1566\n",
      "-------\n",
      "   DAU\n",
      "0   14\n",
      "1  164\n"
     ]
    }
   ],
   "source": [
    "# head() 默认展示前五行\n",
    "print(df.head())\n",
    "print('-------')\n",
    "print(df.head(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 利用shape获取数据表的大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    用户ID 客户分类    区域 是否省会  7月销量  8月销量  9月销量\n",
      "0  59224   A类  一线城市    是     6    20     0\n",
      "1  55295   B类  三线城市    否    37    27    35\n",
      "2  46035   A类  二线城市    是     8     1     8\n",
      "3   2459   C类  一线城市    是     7     8    14\n",
      "4  22179   B类  三线城市    否     9    12     4\n",
      "(5, 7)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df2 = pd.read_table(r'/Users/mc-016/Desktop/对比Excel,轻松学习Python数据分析数据集/train-pivot.csv',\n",
    "                    encoding = 'gbk',sep = ',')\n",
    "print(df2)\n",
    "print(df2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 利用info获取数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5 entries, 0 to 4\n",
      "Data columns (total 7 columns):\n",
      "用户ID    5 non-null int64\n",
      "客户分类    5 non-null object\n",
      "区域      5 non-null object\n",
      "是否省会    5 non-null object\n",
      "7月销量    5 non-null int64\n",
      "8月销量    5 non-null int64\n",
      "9月销量    5 non-null int64\n",
      "dtypes: int64(4), object(3)\n",
      "memory usage: 360.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 利用describe获取数值分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>用户ID</th>\n",
       "      <th>7月销量</th>\n",
       "      <th>8月销量</th>\n",
       "      <th>9月销量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37038.400000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>12.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24098.650249</td>\n",
       "      <td>13.240091</td>\n",
       "      <td>10.163661</td>\n",
       "      <td>13.754999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2459.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22179.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>46035.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55295.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>59224.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               用户ID       7月销量       8月销量       9月销量\n",
       "count      5.000000   5.000000   5.000000   5.000000\n",
       "mean   37038.400000  13.400000  13.600000  12.200000\n",
       "std    24098.650249  13.240091  10.163661  13.754999\n",
       "min     2459.000000   6.000000   1.000000   0.000000\n",
       "25%    22179.000000   7.000000   8.000000   4.000000\n",
       "50%    46035.000000   8.000000  12.000000   8.000000\n",
       "75%    55295.000000   9.000000  20.000000  14.000000\n",
       "max    59224.000000  37.000000  27.000000  35.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理\n",
    "## 缺失值处理\n",
    "    缺失值直接删除或用某个值代替\n",
    "### 缺失值查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\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>用户ID</th>\n",
       "      <th>好坏客户</th>\n",
       "      <th>年龄</th>\n",
       "      <th>负债率</th>\n",
       "      <th>月收入</th>\n",
       "      <th>家属数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.375607</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5710.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.209940</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   用户ID  好坏客户    年龄          负债率      月收入  家属数量\n",
       "0   1.0   1.0  45.0     0.802982   9120.0   2.0\n",
       "1   NaN   NaN   NaN          NaN      NaN   NaN\n",
       "2   3.0   0.0  38.0     0.085113   3042.0   0.0\n",
       "3   4.0   0.0  30.0     0.036050   3300.0   0.0\n",
       "4   5.0   0.0  49.0     0.024926  63588.0   0.0\n",
       "5   6.0   0.0  74.0     0.375607   3500.0   1.0\n",
       "6   7.0   0.0  57.0  5710.000000      NaN   0.0\n",
       "7   8.0   0.0  39.0     0.209940   3500.0   0.0\n",
       "8   9.0   0.0  27.0    46.000000      NaN   NaN"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(r'/Users/mc-016/Desktop/dropna.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9 entries, 0 to 8\n",
      "Data columns (total 6 columns):\n",
      "用户ID    8 non-null float64\n",
      "好坏客户    8 non-null float64\n",
      "年龄      8 non-null float64\n",
      "负债率     8 non-null float64\n",
      "月收入     6 non-null float64\n",
      "家属数量    7 non-null float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 512.0 bytes\n"
     ]
    }
   ],
   "source": [
    "# info 查看是否有缺失值情况\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>用户ID</th>\n",
       "      <th>好坏客户</th>\n",
       "      <th>年龄</th>\n",
       "      <th>负债率</th>\n",
       "      <th>月收入</th>\n",
       "      <th>家属数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    用户ID   好坏客户     年龄    负债率    月收入   家属数量\n",
       "0  False  False  False  False  False  False\n",
       "1   True   True   True   True   True   True\n",
       "2  False  False  False  False  False  False\n",
       "3  False  False  False  False  False  False\n",
       "4  False  False  False  False  False  False\n",
       "5  False  False  False  False  False  False\n",
       "6  False  False  False  False   True  False\n",
       "7  False  False  False  False  False  False\n",
       "8  False  False  False  False   True   True"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# isnull 判断哪个值是缺失值，缺失值返回True，不是缺失值返回False\n",
    "df.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 缺失值删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>63588.0</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.209940</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>0.0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   用户ID  好坏客户    年龄       负债率      月收入  家属数量\n",
       "0   1.0   1.0  45.0  0.802982   9120.0   2.0\n",
       "2   3.0   0.0  38.0  0.085113   3042.0   0.0\n",
       "3   4.0   0.0  30.0  0.036050   3300.0   0.0\n",
       "4   5.0   0.0  49.0  0.024926  63588.0   0.0\n",
       "5   6.0   0.0  74.0  0.375607   3500.0   1.0\n",
       "7   8.0   0.0  39.0  0.209940   3500.0   0.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropna() 默认删除含有缺失值的一整行\n",
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>年龄</th>\n",
       "      <th>负债率</th>\n",
       "      <th>月收入</th>\n",
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       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
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      "text/plain": [
       "   用户ID  好坏客户    年龄          负债率      月收入  家属数量\n",
       "0   1.0   1.0  45.0     0.802982   9120.0   2.0\n",
       "2   3.0   0.0  38.0     0.085113   3042.0   0.0\n",
       "3   4.0   0.0  30.0     0.036050   3300.0   0.0\n",
       "4   5.0   0.0  49.0     0.024926  63588.0   0.0\n",
       "5   6.0   0.0  74.0     0.375607   3500.0   1.0\n",
       "6   7.0   0.0  57.0  5710.000000      NaN   0.0\n",
       "7   8.0   0.0  39.0     0.209940   3500.0   0.0\n",
       "8   9.0   0.0  27.0    46.000000      NaN   NaN"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropna(how = 'all')只删除全为空值的行\n",
    "df.dropna(how = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 缺失值填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>年龄</th>\n",
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       "      <th>月收入</th>\n",
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       "      <td>0.375607</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.209940</td>\n",
       "      <td>3500.0</td>\n",
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       "      <td>9.0</td>\n",
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       "      <td>27.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   用户ID  好坏客户    年龄          负债率      月收入  家属数量\n",
       "0   1.0   1.0  45.0     0.802982   9120.0   2.0\n",
       "1   0.0   0.0   0.0     0.000000      0.0   0.0\n",
       "2   3.0   0.0  38.0     0.085113   3042.0   0.0\n",
       "3   4.0   0.0  30.0     0.036050   3300.0   0.0\n",
       "4   5.0   0.0  49.0     0.024926  63588.0   0.0\n",
       "5   6.0   0.0  74.0     0.375607   3500.0   1.0\n",
       "6   7.0   0.0  57.0  5710.000000      0.0   0.0\n",
       "7   8.0   0.0  39.0     0.209940   3500.0   0.0\n",
       "8   9.0   0.0  27.0    46.000000      0.0   0.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0) #fillna()括号里输入要填充的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "      <th>月收入</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.375607</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5710.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.209940</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   用户ID  好坏客户    年龄          负债率      月收入  家属数量\n",
       "0   1.0   1.0  45.0     0.802982   9120.0   2.0\n",
       "1   2.0   NaN   NaN          NaN      NaN   NaN\n",
       "2   3.0   0.0  38.0     0.085113   3042.0   0.0\n",
       "3   4.0   0.0  30.0     0.036050   3300.0   0.0\n",
       "4   5.0   0.0  49.0     0.024926  63588.0   0.0\n",
       "5   6.0   0.0  74.0     0.375607   3500.0   1.0\n",
       "6   7.0   0.0  57.0  5710.000000      NaN   0.0\n",
       "7   8.0   0.0  39.0     0.209940   3500.0   0.0\n",
       "8   9.0   0.0  27.0    46.000000      NaN   NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 指定列填充\n",
    "df.fillna({'用户ID':2}) #对用户ID填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户ID</th>\n",
       "      <th>好坏客户</th>\n",
       "      <th>年龄</th>\n",
       "      <th>负债率</th>\n",
       "      <th>月收入</th>\n",
       "      <th>家属数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8483.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.375607</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5710.000000</td>\n",
       "      <td>8483.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.209940</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>8483.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   用户ID  好坏客户    年龄          负债率      月收入  家属数量\n",
       "0   1.0   1.0  45.0     0.802982   9120.0   2.0\n",
       "1   2.0   NaN   NaN          NaN   8483.0   NaN\n",
       "2   3.0   0.0  38.0     0.085113   3042.0   0.0\n",
       "3   4.0   0.0  30.0     0.036050   3300.0   0.0\n",
       "4   5.0   0.0  49.0     0.024926  63588.0   0.0\n",
       "5   6.0   0.0  74.0     0.375607   3500.0   1.0\n",
       "6   7.0   0.0  57.0  5710.000000   8483.0   0.0\n",
       "7   8.0   0.0  39.0     0.209940   3500.0   0.0\n",
       "8   9.0   0.0  27.0    46.000000   8483.0   NaN"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 多列填充\n",
    "df.fillna({'用户ID':2,'好坏用户':1,'月收入':8483})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 重复值处理\n",
    "    直接删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>2018/8/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018/8/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018/8/12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码       成交时间\n",
       "0   A1   张桐    101   2018/8/8\n",
       "1   A2   李固    102   2018/8/9\n",
       "2   A3   孙凤    103  2018/8/10\n",
       "3   A3   孙凤    104  2018/8/10\n",
       "4   A4   昭衡    105  2018/8/11\n",
       "5   A5   昭衡    106  2018/8/12"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(r'/Users/mc-016/Desktop/MappingAnalysis_Data.csv',sep = ',',encoding = 'gbk')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>2018/8/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018/8/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018/8/12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码       成交时间\n",
       "0   A1   张桐    101   2018/8/8\n",
       "1   A2   李固    102   2018/8/9\n",
       "3   A3   孙凤    104  2018/8/10\n",
       "4   A4   昭衡    105  2018/8/11\n",
       "5   A5   昭衡    106  2018/8/12"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''drop_duplicates(subset=None, keep='first', inplace=False)\n",
    "subset用来指定特定的列，默认所有列\n",
    "keep='first'表示保留第一次出现的重复行，是默认值。keep另外两个取值为\"last\"和False，分别表示保留最后一次出现的重复行和去除所有重复行。\n",
    "inplace=True表示直接在原来的DataFrame上删除重复项，而默认值False表示生成一个副本。'''\n",
    "\n",
    "df.drop_duplicates(subset = '订单编号',keep = 'first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>A1</td>\n",
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       "      <td>101</td>\n",
       "      <td>2018/8/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018/8/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018/8/12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码       成交时间\n",
       "0   A1   张桐    101   2018/8/8\n",
       "1   A2   李固    102   2018/8/9\n",
       "2   A3   孙凤    103  2018/8/10\n",
       "3   A3   孙凤    104  2018/8/10\n",
       "4   A4   昭衡    105  2018/8/11\n",
       "5   A5   昭衡    106  2018/8/12"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
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       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018-08-09</td>\n",
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       "      <th>3</th>\n",
       "      <td>A3</td>\n",
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       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018-08-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码       成交时间\n",
       "0   A1   张桐    101 2018-08-08\n",
       "1   A2   李固    102 2018-08-09\n",
       "3   A3   孙凤    104 2018-08-10\n",
       "4   A4   昭衡    105 2018-08-11\n",
       "5   A5   昭衡    106 2018-08-12"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(subset = ['订单编号','客户姓名'],keep = 'last',inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>2018/8/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018/8/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018/8/12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码       成交时间\n",
       "0   A1   张桐    101   2018/8/8\n",
       "1   A2   李固    102   2018/8/9\n",
       "3   A3   孙凤    104  2018/8/10\n",
       "4   A4   昭衡    105  2018/8/11\n",
       "5   A5   昭衡    106  2018/8/12"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 异常值的检测与处理\n",
    "### 异常值检测\n",
    "    根据业务经验划定不同指标的正常范围，超过该范围的值算作异常值：\n",
    "    1.通过绘制箱型图，把大于、小于箱型图上下边缘的点称为异常值\n",
    "    2.如果数据服从正态分布：一个数值与平均值之间的偏差超过3倍标准差，那么就是异常值\n",
    "### 异常值处理\n",
    "    1.直接删除异常值\n",
    "    2.异常值当成缺失值填充\n",
    "    3.异常值当作特殊情况，研究异常值出现的原因"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据类型转换\n",
    "### 数据类型\n",
    "    int : 整数\n",
    "    float : 浮点数\n",
    "    object : Python对象类型\n",
    "    string_ : 字符串类型\n",
    "    datatime64[ns] : 时间格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>2018/8/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018/8/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018/8/12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码       成交时间\n",
       "0   A1   张桐    101   2018/8/8\n",
       "1   A2   李固    102   2018/8/9\n",
       "3   A3   孙凤    104  2018/8/10\n",
       "4   A4   昭衡    105  2018/8/11\n",
       "5   A5   昭衡    106  2018/8/12"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dtype 查看数据类型\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['成交时间'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['成交时间']=pd.to_datetime(df['成交时间']) #改成时间格式\n",
    "df['成交时间'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['客户姓名'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['唯一识别码'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 类型转换\n",
    "    astype()数据转换，()要指明转换类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    101.0\n",
       "1    102.0\n",
       "3    104.0\n",
       "4    105.0\n",
       "5    106.0\n",
       "Name: 唯一识别码, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['唯一识别码'].astype('float64') #将int转换为float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2018-08-08\n",
       "1   2018-08-09\n",
       "3   2018-08-10\n",
       "4   2018-08-11\n",
       "5   2018-08-12\n",
       "Name: 成交时间, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['成交时间'].astype('<M8[ns]') #将object转换为datetime64[ns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 索引设置\n",
    "### 为无索引表添加索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>A1</th>\n",
       "      <th>张桐</th>\n",
       "      <th>101</th>\n",
       "      <th>2018-08-08 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018-08-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018-08-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A1  张桐  101 2018-08-08 00:00:00\n",
       "0  A2  李固  102          2018-08-09\n",
       "1  A3  孙凤  103          2018-08-10\n",
       "2  A3  孙凤  104          2018-08-10\n",
       "3  A4  昭衡  105          2018-08-11\n",
       "4  A5  昭衡  106          2018-08-12"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qz = pd.read_excel(r'/Users/mc-016/Desktop/无索引.xlsx')\n",
    "qz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>2018-08-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018-08-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018-08-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1    2          3\n",
       "0  A1  张桐  101 2018-08-08\n",
       "1  A2  李固  102 2018-08-09\n",
       "2  A3  孙凤  103 2018-08-10\n",
       "3  A3  孙凤  104 2018-08-10\n",
       "4  A4  昭衡  105 2018-08-11\n",
       "5  A5  昭衡  106 2018-08-12"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qz = pd.read_excel(r'/Users/mc-016/Desktop/无索引.xlsx',header = None)\n",
    "qz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
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       "      <td>101</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018-08-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018-08-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码       成交时间\n",
       "0   A1   张桐    101 2018-08-08\n",
       "1   A2   李固    102 2018-08-09\n",
       "2   A3   孙凤    103 2018-08-10\n",
       "3   A3   孙凤    104 2018-08-10\n",
       "4   A4   昭衡    105 2018-08-11\n",
       "5   A5   昭衡    106 2018-08-12"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qz.columns = ['订单编号','客户姓名','唯一识别码','成交时间'] #添加列索引\n",
    "qz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>2018-08-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018-08-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018-08-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码       成交时间\n",
       "1   A1   张桐    101 2018-08-08\n",
       "2   A2   李固    102 2018-08-09\n",
       "3   A3   孙凤    103 2018-08-10\n",
       "4   A3   孙凤    104 2018-08-10\n",
       "5   A4   昭衡    105 2018-08-11\n",
       "6   A5   昭衡    106 2018-08-12"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qz.index = [1,2,3,4,5,6] #添加行索引\n",
    "qz "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 重新设置索引\n",
    "    set_index()，()要指明要作行索引的列的名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>订单编号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1</th>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>2018-08-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2</th>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018-08-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3</th>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3</th>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4</th>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A5</th>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018-08-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     客户姓名  唯一识别码       成交时间\n",
       "订单编号                       \n",
       "A1     张桐    101 2018-08-08\n",
       "A2     李固    102 2018-08-09\n",
       "A3     孙凤    103 2018-08-10\n",
       "A3     孙凤    104 2018-08-10\n",
       "A4     昭衡    105 2018-08-11\n",
       "A5     昭衡    106 2018-08-12"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qz.set_index('订单编号')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>客户姓名</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>订单编号</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1</th>\n",
       "      <th>101</th>\n",
       "      <td>张桐</td>\n",
       "      <td>2018-08-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2</th>\n",
       "      <th>102</th>\n",
       "      <td>李固</td>\n",
       "      <td>2018-08-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A3</th>\n",
       "      <th>103</th>\n",
       "      <td>孙凤</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>孙凤</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4</th>\n",
       "      <th>105</th>\n",
       "      <td>昭衡</td>\n",
       "      <td>2018-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A5</th>\n",
       "      <th>106</th>\n",
       "      <td>昭衡</td>\n",
       "      <td>2018-08-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           客户姓名       成交时间\n",
       "订单编号 唯一识别码                \n",
       "A1   101     张桐 2018-08-08\n",
       "A2   102     李固 2018-08-09\n",
       "A3   103     孙凤 2018-08-10\n",
       "     104     孙凤 2018-08-10\n",
       "A4   105     昭衡 2018-08-11\n",
       "A5   106     昭衡 2018-08-12"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qz.set_index(['订单编号','唯一识别码']) #set_index()传入多个列名，层次化索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 重命名索引\n",
    "    rename(),()要指明修改的行索引及列索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>新订单编号</th>\n",
       "      <th>新客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>2018-08-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018-08-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018-08-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  新订单编号 新客户姓名  唯一识别码       成交时间\n",
       "1    A1    张桐    101 2018-08-08\n",
       "2    A2    李固    102 2018-08-09\n",
       "3    A3    孙凤    103 2018-08-10\n",
       "4    A3    孙凤    104 2018-08-10\n",
       "5    A4    昭衡    105 2018-08-11\n",
       "6    A5    昭衡    106 2018-08-12"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qz.rename(columns = {'订单编号':'新订单编号','客户姓名':'新客户姓名'}) #重命名行索引"
   ]
  },
  {
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>2018-08-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>二</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018-08-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>三</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>五</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>六</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018-08-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码       成交时间\n",
       "一   A1   张桐    101 2018-08-08\n",
       "二   A2   李固    102 2018-08-09\n",
       "三   A3   孙凤    103 2018-08-10\n",
       "四   A3   孙凤    104 2018-08-10\n",
       "五   A4   昭衡    105 2018-08-11\n",
       "六   A5   昭衡    106 2018-08-12"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qz.rename(index = {1:'一',2:'二',3:'三',4:'四',5:'五',6:'六'}) #重命名列索引"
   ]
  },
  {
   "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>新订单编号</th>\n",
       "      <th>新客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>2018-08-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>二</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018-08-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>三</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>五</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>六</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018-08-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  新订单编号 新客户姓名  唯一识别码       成交时间\n",
       "一    A1    张桐    101 2018-08-08\n",
       "二    A2    李固    102 2018-08-09\n",
       "三    A3    孙凤    103 2018-08-10\n",
       "四    A3    孙凤    104 2018-08-10\n",
       "五    A4    昭衡    105 2018-08-11\n",
       "六    A5    昭衡    106 2018-08-12"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qz.rename(columns = {'订单编号':'新订单编号','客户姓名':'新客户姓名'},\n",
    "          index = {1:'一',2:'二',3:'三',4:'四',5:'五',6:'六'})  #同时重命名行、列索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 重置索引\n",
    "    reset_index()方法\n",
    "    reset_index(level = None,drop = False,inpalce =False)\n",
    "    level用来指定将层次化索引第几级别转化为columns，第一个索引为0级，第二个索引为1级，默认全部索引\n",
    "    drop用来指定是否删除原索引，False不删除，True删除\n",
    "    inplace用来指定是否修改原数据表，False不修改，True修改"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>客户姓名</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>订单编号</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1</th>\n",
       "      <th>101</th>\n",
       "      <td>张桐</td>\n",
       "      <td>2018-08-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2</th>\n",
       "      <th>102</th>\n",
       "      <td>李固</td>\n",
       "      <td>2018-08-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A3</th>\n",
       "      <th>103</th>\n",
       "      <td>孙凤</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>孙凤</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4</th>\n",
       "      <th>105</th>\n",
       "      <td>昭衡</td>\n",
       "      <td>2018-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A5</th>\n",
       "      <th>106</th>\n",
       "      <td>昭衡</td>\n",
       "      <td>2018-08-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           客户姓名       成交时间\n",
       "订单编号 唯一识别码                \n",
       "A1   101     张桐 2018-08-08\n",
       "A2   102     李固 2018-08-09\n",
       "A3   103     孙凤 2018-08-10\n",
       "     104     孙凤 2018-08-10\n",
       "A4   105     昭衡 2018-08-11\n",
       "A5   106     昭衡 2018-08-12"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qz2 = qz.set_index(['订单编号','唯一识别码'])\n",
    "qz2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>订单编号</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>101</td>\n",
       "      <td>张桐</td>\n",
       "      <td>2018-08-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>102</td>\n",
       "      <td>李固</td>\n",
       "      <td>2018-08-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>103</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>104</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>105</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>2018-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>106</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>2018-08-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号  唯一识别码 客户姓名       成交时间\n",
       "0   A1    101   张桐 2018-08-08\n",
       "1   A2    102   李固 2018-08-09\n",
       "2   A3    103   孙凤 2018-08-10\n",
       "3   A3    104   孙凤 2018-08-10\n",
       "4   A4    105   昭衡 2018-08-11\n",
       "5   A5    106   昭衡 2018-08-12"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qz2.reset_index() #默认将全部index转化为columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>唯一识别码</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>2018-08-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>2018-08-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>2018-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>2018-08-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      订单编号 客户姓名       成交时间\n",
       "唯一识别码                     \n",
       "101     A1   张桐 2018-08-08\n",
       "102     A2   李固 2018-08-09\n",
       "103     A3   孙凤 2018-08-10\n",
       "104     A3   孙凤 2018-08-10\n",
       "105     A4   昭衡 2018-08-11\n",
       "106     A5   昭衡 2018-08-12"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qz2.reset_index(level = 0) # 将第0级索引转化为columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>客户姓名</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张桐</td>\n",
       "      <td>2018-08-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李固</td>\n",
       "      <td>2018-08-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>孙凤</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>孙凤</td>\n",
       "      <td>2018-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>昭衡</td>\n",
       "      <td>2018-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>昭衡</td>\n",
       "      <td>2018-08-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  客户姓名       成交时间\n",
       "0   张桐 2018-08-08\n",
       "1   李固 2018-08-09\n",
       "2   孙凤 2018-08-10\n",
       "3   孙凤 2018-08-10\n",
       "4   昭衡 2018-08-11\n",
       "5   昭衡 2018-08-12"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qz2.reset_index(drop = True) # 将原索引删除，不加入columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据选择\n",
    "## 列选择\n",
    "### 选择某一列/某几列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>2018/8/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018/8/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018/8/12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码       成交时间\n",
       "0   A1   张桐    101   2018/8/8\n",
       "1   A2   李固    102   2018/8/9\n",
       "2   A3   孙凤    103  2018/8/10\n",
       "3   A3   孙凤    104  2018/8/10\n",
       "4   A4   昭衡    105  2018/8/11\n",
       "5   A5   昭衡    106  2018/8/12"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(r'/Users/mc-016/Desktop/对比Excel,轻松学习Python数据分析数据集/MappingAnalysis_Data.csv',\n",
    "                 encoding = 'gbk',sep = ',')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    A1\n",
       "1    A2\n",
       "2    A3\n",
       "3    A3\n",
       "4    A4\n",
       "5    A5\n",
       "Name: 订单编号, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 普通索引\n",
    "df['订单编号']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名\n",
       "0   A1   张桐\n",
       "1   A2   李固\n",
       "2   A3   孙凤\n",
       "3   A3   孙凤\n",
       "4   A4   昭衡\n",
       "5   A5   昭衡"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['订单编号','客户姓名']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>订单编号</th>\n",
       "      <th>唯一识别码</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号  唯一识别码\n",
       "0   A1    101\n",
       "1   A2    102\n",
       "2   A3    103\n",
       "3   A3    104\n",
       "4   A4    105\n",
       "5   A5    106"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取第1列和第3列的数值\n",
    "df.iloc[:,[0,2]]  # 逗号之前的部分表示要获取的行的位置，逗号之后表示获取列的数值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 选择连续的某几列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码\n",
       "0   A1   张桐    101\n",
       "1   A2   李固    102\n",
       "2   A3   孙凤    103\n",
       "3   A3   孙凤    104\n",
       "4   A4   昭衡    105\n",
       "5   A5   昭衡    106"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 切片索引\n",
    "df.iloc[:,0:3] #获取第1列到第4列的位置"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行选择\n",
    "### 选择某一行/某几行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>2018/8/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>二</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>三</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>五</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018/8/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>六</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018/8/12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码       成交时间\n",
       "一   A1   张桐    101   2018/8/8\n",
       "二   A2   李固    102   2018/8/9\n",
       "三   A3   孙凤    103  2018/8/10\n",
       "四   A3   孙凤    104  2018/8/10\n",
       "五   A4   昭衡    105  2018/8/11\n",
       "六   A5   昭衡    106  2018/8/12"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 利用loc方法\n",
    "df2 = df.rename(index = {0:'一',1:'二',2:'三',3:'四',4:'五',5:'六'})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "订单编号           A1\n",
       "客户姓名           张桐\n",
       "唯一识别码         101\n",
       "成交时间     2018/8/8\n",
       "Name: 一, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc['一'] #选取一行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>2018/8/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>二</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码      成交时间\n",
       "一   A1   张桐    101  2018/8/8\n",
       "二   A2   李固    102  2018/8/9"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[['一','二']] #选择第一行和第二行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "订单编号           A1\n",
       "客户姓名           张桐\n",
       "唯一识别码         101\n",
       "成交时间     2018/8/8\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 利用iloc方法\n",
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码       成交时间\n",
       "1   A2   李固    102   2018/8/9\n",
       "3   A3   孙凤    104  2018/8/10"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1,3]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 选择连续的几行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码       成交时间\n",
       "1   A2   李固    102   2018/8/9\n",
       "2   A3   孙凤    103  2018/8/10"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取满足条件的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>2018/8/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018/8/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018/8/12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码       成交时间\n",
       "0   A1   张桐    101   2018/8/8\n",
       "1   A2   李固    102   2018/8/9\n",
       "2   A3   孙凤    103  2018/8/10\n",
       "3   A3   孙凤    104  2018/8/10\n",
       "4   A4   昭衡    105  2018/8/11\n",
       "5   A5   昭衡    106  2018/8/12"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>2018/8/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>2018/8/12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码       成交时间\n",
       "3   A3   孙凤    104  2018/8/10\n",
       "4   A4   昭衡    105  2018/8/11\n",
       "5   A5   昭衡    106  2018/8/12"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['唯一识别码'] > 103] #选择唯一识别码 > 103 的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'<' not supported between instances of 'str' and 'float'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-0bacd9c871fe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'唯一识别码'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m103\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'成交时间'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m2018\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m12\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[0m",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, other, axis)\u001b[0m\n\u001b[1;32m   1251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1252\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1253\u001b[0;31m                 \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mna_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1254\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1255\u001b[0m                 raise TypeError('Could not compare {typ} type with Series'\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mna_op\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m   1138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1139\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1140\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_comp_method_OBJECT_ARRAY\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1142\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mis_datetimelike_v_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36m_comp_method_OBJECT_ARRAY\u001b[0;34m(op, x, y)\u001b[0m\n\u001b[1;32m   1117\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvec_compare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1118\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1119\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscalar_compare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1120\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/ops.pyx\u001b[0m in \u001b[0;36mpandas._libs.ops.scalar_compare\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'str' and 'float'"
     ]
    }
   ],
   "source": [
    "df[(df['唯一识别码'] > 103) & (df['成交时间'] < (2018/8/12))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行列同时选择\n",
    "### 普通索引+普通索引选择指定的行和列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>订单编号</th>\n",
       "      <th>唯一识别码</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一</th>\n",
       "      <td>A1</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>三</th>\n",
       "      <td>A3</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号  唯一识别码\n",
       "一   A1    101\n",
       "三   A3    103"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[['一','三'],['订单编号','唯一识别码']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 位置索引+位置索引选择指定的行和列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>订单编号</th>\n",
       "      <th>唯一识别码</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一</th>\n",
       "      <td>A1</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>二</th>\n",
       "      <td>A2</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号  唯一识别码\n",
       "一   A1    101\n",
       "二   A2    102"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.iloc[[0,1],[0,2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 布尔索引+普通索引选择指定的行列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>四</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>五</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>六</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名\n",
       "四   A3   孙凤\n",
       "五   A4   昭衡\n",
       "六   A5   昭衡"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['唯一识别码'] > 103][['订单编号','客户姓名']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 切片索引+切片索引选择指定行列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>二</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>三</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码\n",
       "一   A1   张桐    101\n",
       "二   A2   李固    102\n",
       "三   A3   孙凤    103"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.iloc[0:3,0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数值操作\n",
    "## 数值替换\n",
    "### 一对一替换\n",
    "    replace(A,B)表示将A替换成B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>31</td>\n",
       "      <td>2018/8/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>45</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>240</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>240</td>\n",
       "      <td>2018/8/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码   年龄       成交时间\n",
       "0   A1   张桐    101   31   2018/8/8\n",
       "1   A2   李固    102   45   2018/8/9\n",
       "2   A3   孙凤    103   23  2018/8/10\n",
       "3   A3   孙凤    104  240  2018/8/10\n",
       "4   A4   昭衡    105  240  2018/8/11\n",
       "5   A5   昭衡    106   23  2018/8/12"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "df = pd.read_csv(r'/Users/mc-016/Desktop/对比Excel,轻松学习Python数据分析数据集/MappingAnalysis_Data.csv',\n",
    "                encoding = 'gbk',sep = ',')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>31</td>\n",
       "      <td>2018/8/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>45</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>33</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>33</td>\n",
       "      <td>2018/8/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄       成交时间\n",
       "0   A1   张桐    101  31   2018/8/8\n",
       "1   A2   李固    102  45   2018/8/9\n",
       "2   A3   孙凤    103  23  2018/8/10\n",
       "3   A3   孙凤    104  33  2018/8/10\n",
       "4   A4   昭衡    105  33  2018/8/11\n",
       "5   A5   昭衡    106  23  2018/8/12"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace(240,33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>否</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
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      ],
      "text/plain": [
       "    用户ID 客户分类    区域 是否省会  7月销量  8月销量  9月销量\n",
       "0  59224   A类  一线城市    是   6.0  20.0   0.0\n",
       "1  55295   B类  三线城市    否  37.0  27.0   NaN\n",
       "2  46035   A类  二线城市    是   8.0   1.0   8.0\n",
       "3   2459   C类  一线城市    是   7.0   NaN  14.0\n",
       "4  22179   B类  三线城市    否   NaN  12.0   4.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace()对缺失值替换相当于fillna()\n",
    "df2 = pd.read_table(r'/Users/mc-016/Desktop/对比Excel,轻松学习Python数据分析数据集/新建应用2.txt',sep = '\\t' )\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>2459</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    用户ID 客户分类    区域 是否省会  7月销量  8月销量  9月销量\n",
       "0  59224   A类  一线城市    是   6.0  20.0   0.0\n",
       "1  55295   B类  三线城市    否  37.0  27.0   0.0\n",
       "2  46035   A类  二线城市    是   8.0   1.0   8.0\n",
       "3   2459   C类  一线城市    是   7.0   0.0  14.0\n",
       "4  22179   B类  三线城市    否   0.0  12.0   4.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df2.replace(np.NaN,0) #np.NaN是python中表示缺失值的一种方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>2459</td>\n",
       "      <td>C类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
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      ],
      "text/plain": [
       "    用户ID 客户分类    区域 是否省会  7月销量  8月销量  9月销量\n",
       "0  59224   A类  一线城市    是   6.0  20.0   0.0\n",
       "1  55295   B类  三线城市    否  37.0  27.0   NaN\n",
       "2  46035   A类  二线城市    是   8.0   1.0   8.0\n",
       "3   2459   C类  一线城市    是   7.0   NaN  14.0\n",
       "4  22179   B类  三线城市    否   NaN  12.0   4.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'fiilna'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-7325aa4118e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfiilna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   4370\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4371\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4372\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4374\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'fiilna'"
     ]
    }
   ],
   "source": [
    "df2.fiilna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     A    B   C  D\n",
       "0  NaN  2.0 NaN  0\n",
       "1  3.0  4.0 NaN  1\n",
       "2  NaN  NaN NaN  5\n",
       "3  NaN  3.0 NaN  4"
      ]
     },
     "execution_count": 18,
     "metadata": {},
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    }
   ],
   "source": [
    "df3 = pd.DataFrame([[np.nan,2,np.nan,0],\n",
    "                   [3,4,np.nan,1],\n",
    "                   [np.nan,np.nan,np.nan,5],\n",
    "                   [np.nan,3,np.nan,4]],\n",
    "                   columns=list('ABCD'))\n",
    "df3"
   ]
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  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C  D\n",
       "0  0.0  2.0  0.0  0\n",
       "1  3.0  4.0  0.0  1\n",
       "2  0.0  0.0  0.0  5\n",
       "3  0.0  3.0  0.0  4"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多对一替换\n",
    "    replace([A,B],C)表示将A、B替换成C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>31</td>\n",
       "      <td>2018/8/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>45</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>240</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>240</td>\n",
       "      <td>2018/8/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码   年龄       成交时间\n",
       "0   A1   张桐    101   31   2018/8/8\n",
       "1   A2   李固    102   45   2018/8/9\n",
       "2   A3   孙凤    103   23  2018/8/10\n",
       "3   A3   孙凤    104  240  2018/8/10\n",
       "4   A4   昭衡    105  240  2018/8/11\n",
       "5   A5   昭衡    106   23  2018/8/12"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>31</td>\n",
       "      <td>2018/8/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>33</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>33</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>33</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>33</td>\n",
       "      <td>2018/8/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>33</td>\n",
       "      <td>2018/8/12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄       成交时间\n",
       "0   A1   张桐    101  31   2018/8/8\n",
       "1   A2   李固    102  33   2018/8/9\n",
       "2   A3   孙凤    103  33  2018/8/10\n",
       "3   A3   孙凤    104  33  2018/8/10\n",
       "4   A4   昭衡    105  33  2018/8/11\n",
       "5   A5   昭衡    106  33  2018/8/12"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace([240,23,45],33)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多对多替换\n",
    "    replace({'A':'a','B':'b'})表示用a替换A，用b替换B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>31</td>\n",
       "      <td>2018/8/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>35</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>34</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>33</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>33</td>\n",
       "      <td>2018/8/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>34</td>\n",
       "      <td>2018/8/12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄       成交时间\n",
       "0   A1   张桐    101  31   2018/8/8\n",
       "1   A2   李固    102  35   2018/8/9\n",
       "2   A3   孙凤    103  34  2018/8/10\n",
       "3   A3   孙凤    104  33  2018/8/10\n",
       "4   A4   昭衡    105  33  2018/8/11\n",
       "5   A5   昭衡    106  34  2018/8/12"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace({240:33,23:34,45:35})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数值排序\n",
    "### 按照一列数值进行排序\n",
    "    df.sort_values(by = ['coll'],ascending =False)\n",
    "    coll代表列，ascending = False表示按照coll列降序排列，ascending默认为True,表示升序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>31</td>\n",
       "      <td>2018/8/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>45</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>240</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>240</td>\n",
       "      <td>2018/8/11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码   年龄       成交时间\n",
       "2   A3   孙凤    103   23  2018/8/10\n",
       "5   A5   昭衡    106   23  2018/8/12\n",
       "0   A1   张桐    101   31   2018/8/8\n",
       "1   A2   李固    102   45   2018/8/9\n",
       "3   A3   孙凤    104  240  2018/8/10\n",
       "4   A4   昭衡    105  240  2018/8/11"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by = ['年龄']) #按年龄升序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>成交时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>104</td>\n",
       "      <td>240</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>105</td>\n",
       "      <td>240</td>\n",
       "      <td>2018/8/11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>45</td>\n",
       "      <td>2018/8/9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>31</td>\n",
       "      <td>2018/8/8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>106</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码   年龄       成交时间\n",
       "3   A3   孙凤    104  240  2018/8/10\n",
       "4   A4   昭衡    105  240  2018/8/11\n",
       "1   A2   李固    102   45   2018/8/9\n",
       "0   A1   张桐    101   31   2018/8/8\n",
       "2   A3   孙凤    103   23  2018/8/10\n",
       "5   A5   昭衡    106   23  2018/8/12"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by = ['年龄'],ascending = False) #按年龄降序"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 按照有缺失值的列进行排序\n",
    "    na_position对缺失值进行设置，默认参数值last，表示将缺失值排在最后"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>用户ID</th>\n",
       "      <th>客户分类</th>\n",
       "      <th>区域</th>\n",
       "      <th>是否省会</th>\n",
       "      <th>7月销量</th>\n",
       "      <th>8月销量</th>\n",
       "      <th>9月销量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59224</td>\n",
       "      <td>A类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55295</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>37.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46035</td>\n",
       "      <td>A类</td>\n",
       "      <td>二线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2459</td>\n",
       "      <td>C类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22179</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    用户ID 客户分类    区域 是否省会  7月销量  8月销量  9月销量\n",
       "0  59224   A类  一线城市    是   6.0  20.0   0.0\n",
       "1  55295   B类  三线城市    否  37.0  27.0   NaN\n",
       "2  46035   A类  二线城市    是   8.0   1.0   8.0\n",
       "3   2459   C类  一线城市    是   7.0   NaN  14.0\n",
       "4  22179   B类  三线城市    否   NaN  12.0   4.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_table(r'/Users/mc-016/Desktop/对比Excel,轻松学习Python数据分析数据集/新建应用2.txt',sep = '\\t')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户ID</th>\n",
       "      <th>客户分类</th>\n",
       "      <th>区域</th>\n",
       "      <th>是否省会</th>\n",
       "      <th>7月销量</th>\n",
       "      <th>8月销量</th>\n",
       "      <th>9月销量</th>\n",
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       "      <td>59224</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2459</td>\n",
       "      <td>C类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
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       "      <td>14.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>46035</td>\n",
       "      <td>A类</td>\n",
       "      <td>二线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55295</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>37.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22179</td>\n",
       "      <td>B类</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>否</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    用户ID 客户分类    区域 是否省会  7月销量  8月销量  9月销量\n",
       "0  59224   A类  一线城市    是   6.0  20.0   0.0\n",
       "3   2459   C类  一线城市    是   7.0   NaN  14.0\n",
       "2  46035   A类  二线城市    是   8.0   1.0   8.0\n",
       "1  55295   B类  三线城市    否  37.0  27.0   NaN\n",
       "4  22179   B类  三线城市    否   NaN  12.0   4.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by = ['7月销量'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>区域</th>\n",
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       "      <th>7月销量</th>\n",
       "      <th>8月销量</th>\n",
       "      <th>9月销量</th>\n",
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       "      <td>22179</td>\n",
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       "      <td>否</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46035</td>\n",
       "      <td>A类</td>\n",
       "      <td>二线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2459</td>\n",
       "      <td>C类</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>是</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    用户ID 客户分类    区域 是否省会  7月销量  8月销量  9月销量\n",
       "1  55295   B类  三线城市    否  37.0  27.0   NaN\n",
       "0  59224   A类  一线城市    是   6.0  20.0   0.0\n",
       "4  22179   B类  三线城市    否   NaN  12.0   4.0\n",
       "2  46035   A类  二线城市    是   8.0   1.0   8.0\n",
       "3   2459   C类  一线城市    是   7.0   NaN  14.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by = ['9月销量'],na_position = 'first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
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       "      <td>2018/8/8</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>45</td>\n",
       "      <td>2018/8/9</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>104</td>\n",
       "      <td>36</td>\n",
       "      <td>2018/8/11</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A5</td>\n",
       "      <td>王娜</td>\n",
       "      <td>105</td>\n",
       "      <td>21</td>\n",
       "      <td>2018/8/12</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄       成交时间  销售ID\n",
       "0   A1   张桐    101  31   2018/8/8   1.0\n",
       "1   A2   李固    102  45   2018/8/9   2.0\n",
       "2   A3   孙凤    103  23  2018/8/10   NaN\n",
       "3   A4   昭衡    104  36  2018/8/11   2.0\n",
       "4   A5   王娜    105  21  2018/8/12   3.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv(r'/Users/mc-016/Desktop/对比Excel,轻松学习Python数据分析数据集/MappingAnalysis_Data.csv',\n",
    "                 encoding = 'gbk',sep = ',')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>A5</td>\n",
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       "      <td>105</td>\n",
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       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄       成交时间  销售ID\n",
       "0   A1   张桐    101  31   2018/8/8   1.0\n",
       "1   A2   李固    102  45   2018/8/9   2.0\n",
       "3   A4   昭衡    104  36  2018/8/11   2.0\n",
       "4   A5   王娜    105  21  2018/8/12   3.0\n",
       "2   A3   孙凤    103  23  2018/8/10   NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sort_values(by = ['销售ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>A5</td>\n",
       "      <td>王娜</td>\n",
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       "      <td>3.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄       成交时间  销售ID\n",
       "2   A3   孙凤    103  23  2018/8/10   NaN\n",
       "0   A1   张桐    101  31   2018/8/8   1.0\n",
       "1   A2   李固    102  45   2018/8/9   2.0\n",
       "3   A4   昭衡    104  36  2018/8/11   2.0\n",
       "4   A5   王娜    105  21  2018/8/12   3.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sort_values(by = ['销售ID'],na_position = 'first')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 按照多列数值进行排序\n",
    "    df.sort_values(by = ['col1','col2'],ascending = [True,False])\n",
    "    表示先按照col1升序，当col1遇到重复值按col2降序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>成交时间</th>\n",
       "      <th>销售ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>31</td>\n",
       "      <td>2018/8/8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>45</td>\n",
       "      <td>2018/8/9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>104</td>\n",
       "      <td>36</td>\n",
       "      <td>2018/8/11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A5</td>\n",
       "      <td>王娜</td>\n",
       "      <td>105</td>\n",
       "      <td>21</td>\n",
       "      <td>2018/8/12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄       成交时间  销售ID\n",
       "0   A1   张桐    101  31   2018/8/8     1\n",
       "1   A2   李固    102  45   2018/8/9     2\n",
       "2   A3   孙凤    103  23  2018/8/10     1\n",
       "3   A4   昭衡    104  36  2018/8/11     2\n",
       "4   A5   王娜    105  21  2018/8/12     3"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'/Users/mc-016/Desktop/对比Excel,轻松学习Python数据分析数据集/MappingAnalysis_Data.csv',\n",
    "                encoding = 'gbk',sep = ',')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>成交时间</th>\n",
       "      <th>销售ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>31</td>\n",
       "      <td>2018/8/8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>104</td>\n",
       "      <td>36</td>\n",
       "      <td>2018/8/11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>45</td>\n",
       "      <td>2018/8/9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A5</td>\n",
       "      <td>王娜</td>\n",
       "      <td>105</td>\n",
       "      <td>21</td>\n",
       "      <td>2018/8/12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄       成交时间  销售ID\n",
       "2   A3   孙凤    103  23  2018/8/10     1\n",
       "0   A1   张桐    101  31   2018/8/8     1\n",
       "3   A4   昭衡    104  36  2018/8/11     2\n",
       "1   A2   李固    102  45   2018/8/9     2\n",
       "4   A5   王娜    105  21  2018/8/12     3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by = ['销售ID','订单编号'],ascending = [True,False])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数值排名\n",
    "### Excel实现\n",
    "    在excel中用于排名的函数有RANK.AVG()\\RANK.EQ()两个，两个函数的不同在于处理重复值方式不同\n",
    "    RANK.AVG(number,ref,order)：number表示待排名的数值，ref表示一整列数值的范围，order表示指明降序还是升序，当排名有重复值时返回重复值的平均排名；\n",
    "    RANK.EQ(number,ref,order)：number表示待排名的数值，ref表示一整列数值的范围，order表示指明降序还是升序，当排名有重复值时返回重复值的最佳排名\n",
    "### python实现\n",
    "    rank(method,ascending):ascending用来指明升序或是降序，默认升序；\n",
    "    merhod可取的不同参数值说明：\n",
    "    average：与Excel中的RANK.AVG函数功能一样\n",
    "    first：按值在所有的待排列数据中出现的先后顺序排名\n",
    "    min：与Excel中的RANK.EQ函数功能一样\n",
    "    max：与min相反，取重复值的最大排名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>成交时间</th>\n",
       "      <th>销售ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>31</td>\n",
       "      <td>2018/8/8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>45</td>\n",
       "      <td>2018/8/9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>104</td>\n",
       "      <td>36</td>\n",
       "      <td>2018/8/11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A5</td>\n",
       "      <td>王娜</td>\n",
       "      <td>105</td>\n",
       "      <td>21</td>\n",
       "      <td>2018/8/12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄       成交时间  销售ID\n",
       "0   A1   张桐    101  31   2018/8/8     1\n",
       "1   A2   李固    102  45   2018/8/9     2\n",
       "2   A3   孙凤    103  23  2018/8/10     1\n",
       "3   A4   昭衡    104  36  2018/8/11     2\n",
       "4   A5   王娜    105  21  2018/8/12     3"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(r'/Users/mc-016/Desktop/对比Excel,轻松学习Python数据分析数据集/MappingAnalysis_Data.csv',\n",
    "                encoding = 'gbk')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1.5\n",
      "1    3.5\n",
      "2    1.5\n",
      "3    3.5\n",
      "4    5.0\n",
      "Name: 销售ID, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    4.5\n",
       "1    2.5\n",
       "2    4.5\n",
       "3    2.5\n",
       "4    1.0\n",
       "Name: 销售ID, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# method取值average\n",
    "print(df['销售ID'].rank(method = 'average'))\n",
    "df['销售ID'].rank(method = 'average',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    2.0\n",
       "3    4.0\n",
       "4    5.0\n",
       "Name: 销售ID, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# method取值first\n",
    "df['销售ID'].rank(method = 'first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    1.0\n",
       "3    3.0\n",
       "4    5.0\n",
       "Name: 销售ID, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# method取值min\n",
    "df['销售ID'].rank(method = 'min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "1    4.0\n",
       "2    2.0\n",
       "3    4.0\n",
       "4    5.0\n",
       "Name: 销售ID, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# method取值max\n",
    "df['销售ID'].rank(method = 'max')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数值删除\n",
    "### 删除列\n",
    "    drop()方法指明要删除的列或列的位置\n",
    "    直接传入待删除的列的列名，需要加一个参数axis = 1，表示删除列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>104</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A5</td>\n",
       "      <td>王娜</td>\n",
       "      <td>105</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄\n",
       "0   A1   张桐    101  31\n",
       "1   A2   李固    102  45\n",
       "2   A3   孙凤    103  23\n",
       "3   A4   昭衡    104  36\n",
       "4   A5   王娜    105  21"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['销售ID','成交时间'],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>104</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A5</td>\n",
       "      <td>王娜</td>\n",
       "      <td>105</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄\n",
       "0   A1   张桐    101  31\n",
       "1   A2   李固    102  45\n",
       "2   A3   孙凤    103  23\n",
       "3   A4   昭衡    104  36\n",
       "4   A5   王娜    105  21"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除第5列和第6列\n",
    "df.drop(df.columns[[4,5]],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>104</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A5</td>\n",
       "      <td>王娜</td>\n",
       "      <td>105</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄\n",
       "0   A1   张桐    101  31\n",
       "1   A2   李固    102  45\n",
       "2   A3   孙凤    103  23\n",
       "3   A4   昭衡    104  36\n",
       "4   A5   王娜    105  21"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns = ['销售ID','成交时间'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 删除行\n",
    "    drop()方法指明要删除的行或行的位置\n",
    "    直接传入待删除的行的行名，需要加一个参数axis = 0，表示删除行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>成交时间</th>\n",
       "      <th>销售ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>31</td>\n",
       "      <td>2018/8/8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>45</td>\n",
       "      <td>2018/8/9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>104</td>\n",
       "      <td>36</td>\n",
       "      <td>2018/8/11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>A5</td>\n",
       "      <td>王娜</td>\n",
       "      <td>105</td>\n",
       "      <td>21</td>\n",
       "      <td>2018/8/12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄       成交时间  销售ID\n",
       "a   A1   张桐    101  31   2018/8/8     1\n",
       "b   A2   李固    102  45   2018/8/9     2\n",
       "c   A3   孙凤    103  23  2018/8/10     1\n",
       "d   A4   昭衡    104  36  2018/8/11     2\n",
       "e   A5   王娜    105  21  2018/8/12     3"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename(index = {0:'a',1:'b',2:'c',3:'d',4:'e'})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>成交时间</th>\n",
       "      <th>销售ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>104</td>\n",
       "      <td>36</td>\n",
       "      <td>2018/8/11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>A5</td>\n",
       "      <td>王娜</td>\n",
       "      <td>105</td>\n",
       "      <td>21</td>\n",
       "      <td>2018/8/12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄       成交时间  销售ID\n",
       "c   A3   孙凤    103  23  2018/8/10     1\n",
       "d   A4   昭衡    104  36  2018/8/11     2\n",
       "e   A5   王娜    105  21  2018/8/12     3"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['a','b'],axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>成交时间</th>\n",
       "      <th>销售ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>104</td>\n",
       "      <td>36</td>\n",
       "      <td>2018/8/11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>A5</td>\n",
       "      <td>王娜</td>\n",
       "      <td>105</td>\n",
       "      <td>21</td>\n",
       "      <td>2018/8/12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄       成交时间  销售ID\n",
       "c   A3   孙凤    103  23  2018/8/10     1\n",
       "d   A4   昭衡    104  36  2018/8/11     2\n",
       "e   A5   王娜    105  21  2018/8/12     3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除第一行和第二行\n",
    "df.drop(df.index[[0,1]],axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>成交时间</th>\n",
       "      <th>销售ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>104</td>\n",
       "      <td>36</td>\n",
       "      <td>2018/8/11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>A5</td>\n",
       "      <td>王娜</td>\n",
       "      <td>105</td>\n",
       "      <td>21</td>\n",
       "      <td>2018/8/12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄       成交时间  销售ID\n",
       "c   A3   孙凤    103  23  2018/8/10     1\n",
       "d   A4   昭衡    104  36  2018/8/11     2\n",
       "e   A5   王娜    105  21  2018/8/12     3"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 直接将要删除的行名传给index参数\n",
    "df.drop(index = ['a','b'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 删除特定行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>成交时间</th>\n",
       "      <th>销售ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>31</td>\n",
       "      <td>2018/8/8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>104</td>\n",
       "      <td>36</td>\n",
       "      <td>2018/8/11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>A5</td>\n",
       "      <td>王娜</td>\n",
       "      <td>105</td>\n",
       "      <td>21</td>\n",
       "      <td>2018/8/12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄       成交时间  销售ID\n",
       "a   A1   张桐    101  31   2018/8/8     1\n",
       "c   A3   孙凤    103  23  2018/8/10     1\n",
       "d   A4   昭衡    104  36  2018/8/11     2\n",
       "e   A5   王娜    105  21  2018/8/12     3"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除年龄值大于等于40对应的行，取相反部分出来\n",
    "df[df['年龄'] < 40]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数值计数\n",
    "### Excel实现\n",
    "    =COUNTIF(range,criteria)\n",
    "    range 表示一系列值的范围\n",
    "    criteria 表示某一值或某一条件\n",
    "### python实现\n",
    "    value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    2\n",
       "1    2\n",
       "3    1\n",
       "Name: 销售ID, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['销售ID'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    0.4\n",
       "1    0.4\n",
       "3    0.2\n",
       "Name: 销售ID, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 不同值占比\n",
    "df['销售ID'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.4\n",
       "2    0.4\n",
       "3    0.2\n",
       "Name: 销售ID, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort = False 降序排列\n",
    "df['销售ID'].value_counts(normalize = True,sort = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 唯一值获取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['销售ID'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数值查找"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>订单编号</th>\n",
       "      <th>客户姓名</th>\n",
       "      <th>唯一识别码</th>\n",
       "      <th>年龄</th>\n",
       "      <th>成交时间</th>\n",
       "      <th>销售ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>张桐</td>\n",
       "      <td>101</td>\n",
       "      <td>31</td>\n",
       "      <td>2018/8/8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>李固</td>\n",
       "      <td>102</td>\n",
       "      <td>45</td>\n",
       "      <td>2018/8/9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>孙凤</td>\n",
       "      <td>103</td>\n",
       "      <td>23</td>\n",
       "      <td>2018/8/10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>昭衡</td>\n",
       "      <td>104</td>\n",
       "      <td>36</td>\n",
       "      <td>2018/8/11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A5</td>\n",
       "      <td>王娜</td>\n",
       "      <td>105</td>\n",
       "      <td>21</td>\n",
       "      <td>2018/8/12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  订单编号 客户姓名  唯一识别码  年龄       成交时间  销售ID\n",
       "0   A1   张桐    101  31   2018/8/8     1\n",
       "1   A2   李固    102  45   2018/8/9     2\n",
       "2   A3   孙凤    103  23  2018/8/10     1\n",
       "3   A4   昭衡    104  36  2018/8/11     2\n",
       "4   A5   王娜    105  21  2018/8/12     3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4     True\n",
       "Name: 年龄, dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 年龄这一列是否包含 31 21 两个值\n",
    "df['年龄'].isin([31,21])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>年龄</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    订单编号   客户姓名  唯一识别码     年龄   成交时间   销售ID\n",
       "0  False  False  False   True  False  False\n",
       "1  False  False  False  False  False  False\n",
       "2  False  False  False  False  False  False\n",
       "3  False  False  False  False  False  False\n",
       "4  False  False  False   True  False  False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 全表是否包含 31 21 两个值\n",
    "df.isin([31,21])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 区间切分\n",
    "### Excel实现\n",
    "    =IF(D2<4,'<4',IF(D2<7,'4-6','>=7'))\n",
    "### Python实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年龄</th>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
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       "      <th>6</th>\n",
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       "      <th>7</th>\n",
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       "      <th>8</th>\n",
       "      <td>9</td>\n",
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       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   年龄\n",
       "0   1\n",
       "1   2\n",
       "2   3\n",
       "3   4\n",
       "4   5\n",
       "5   6\n",
       "6   7\n",
       "7   8\n",
       "8   9\n",
       "9  10"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame([1,2,3,4,5,6,7,8,9,10],columns = ['年龄'])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     (0, 3]\n",
       "1     (0, 3]\n",
       "2     (0, 3]\n",
       "3     (3, 6]\n",
       "4     (3, 6]\n",
       "5     (3, 6]\n",
       "6    (6, 10]\n",
       "7    (6, 10]\n",
       "8    (6, 10]\n",
       "9    (6, 10]\n",
       "Name: 年龄, dtype: category\n",
       "Categories (3, interval[int64]): [(0, 3] < (3, 6] < (6, 10]]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(df2['年龄'],bins = [0,3,6,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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