Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3fad61f7-b58c-4799-8dd2-6567deb2639b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv('./data/douban.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e91b03",
   "metadata": {},
   "source": [
    "# 设置数据输出时对齐\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ef32ec8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             电影名        状态  评分  星级   \n",
      "0                              狗神  nowplaying   8.3   4.5  \\\n",
      "1                            三大队  nowplaying   7.9   4.0   \n",
      "2                              怒潮  nowplaying   NaN   NaN   \n",
      "3            名侦探柯南：黑铁的鱼影  nowplaying   6.7   3.5   \n",
      "4                              旺卡  nowplaying   7.6   4.0   \n",
      "5                      涉过愤怒的海  nowplaying   7.5   4.0   \n",
      "6                          瞒天过海  nowplaying   6.3   3.5   \n",
      "7                          照明商店  nowplaying   6.5   3.5   \n",
      "8                            爆裂点  nowplaying   6.5   3.5   \n",
      "9                          月光武士  nowplaying   NaN   NaN   \n",
      "10           饥饿游戏：鸣鸟与蛇之歌  nowplaying   7.0   3.5   \n",
      "11                       我本是高山  nowplaying   NaN   NaN   \n",
      "12                     再见，李可乐  nowplaying   6.7   3.5   \n",
      "13                       河边的错误  nowplaying   7.3   4.0   \n",
      "14                           拿破仑  nowplaying   6.5   3.5   \n",
      "15   蜡笔小新：新次元！超能力大决战  nowplaying   8.1   4.0   \n",
      "16                             热搜  nowplaying   6.3   3.5   \n",
      "17                       飞鸭向前冲  nowplaying   NaN   NaN   \n",
      "18                             星愿  nowplaying   6.7   3.5   \n",
      "19                             刀尖  nowplaying   6.0   3.0   \n",
      "20                             红猪  nowplaying   8.6   4.5   \n",
      "21                         无价之宝  nowplaying   6.5   3.5   \n",
      "22                  前任4：英年早婚  nowplaying   5.7   3.0   \n",
      "23                         沉默笔录  nowplaying   6.9   3.5   \n",
      "24                             仲肯  nowplaying   NaN   NaN   \n",
      "25                   不要走散好不好  nowplaying   NaN   NaN   \n",
      "26                           诡摇铃  nowplaying   NaN   NaN   \n",
      "27                         二手杰作  nowplaying   6.7   3.5   \n",
      "28                         吾爱敦煌  nowplaying   7.1   3.5   \n",
      "29                         最后一夜  nowplaying   NaN   NaN   \n",
      "30                       靠近我一点  nowplaying   4.1   2.0   \n",
      "31                         铃芽之旅  nowplaying   7.2   4.0   \n",
      "32           迷你特工队之汉堡大作战  nowplaying   NaN   NaN   \n",
      "33  汪汪队立大功大电影2：超能大冒险  nowplaying   7.5   4.0   \n",
      "34                       最好的相遇  nowplaying   6.3   3.5   \n",
      "35                         千里送鹤  nowplaying   NaN   NaN   \n",
      "36              我是哪吒2之英雄归来  nowplaying   2.4   1.5   \n",
      "37                 贝肯熊：火星任务  nowplaying   7.2   3.5   \n",
      "38                 功夫王之萌虎上山  nowplaying   NaN   NaN   \n",
      "39                海王2：失落的王国    upcoming   NaN   NaN   \n",
      "40                         爱乐之城    upcoming   NaN   NaN   \n",
      "41                         敦煌英雄    upcoming   NaN   NaN   \n",
      "42                         人民万岁    upcoming   NaN   NaN   \n",
      "43                             潜行    upcoming   NaN   NaN   \n",
      "44                     年会不能停！    upcoming   NaN   NaN   \n",
      "45                     金色面具英雄    upcoming   NaN   NaN   \n",
      "46                           金手指    upcoming   NaN   NaN   \n",
      "47                   一闪一闪亮星星    upcoming   NaN   NaN   \n",
      "48                        非诚勿扰3    upcoming   NaN   NaN   \n",
      "\n",
      "                 时长   \n",
      "0             114分钟  \\\n",
      "1             132分钟   \n",
      "2             106分钟   \n",
      "3             110分钟   \n",
      "4             116分钟   \n",
      "5             144分钟   \n",
      "6             105分钟   \n",
      "7              99分钟   \n",
      "8             138分钟   \n",
      "9             110分钟   \n",
      "10            158分钟   \n",
      "11            131分钟   \n",
      "12            105分钟   \n",
      "13            102分钟   \n",
      "14  157分钟(中国大陆)   \n",
      "15             94分钟   \n",
      "16            121分钟   \n",
      "17             82分钟   \n",
      "18             95分钟   \n",
      "19            121分钟   \n",
      "20             94分钟   \n",
      "21            132分钟   \n",
      "22            129分钟   \n",
      "23             91分钟   \n",
      "24             87分钟   \n",
      "25             99分钟   \n",
      "26             85分钟   \n",
      "27            114分钟   \n",
      "28            101分钟   \n",
      "29             89分钟   \n",
      "30             94分钟   \n",
      "31  122分钟(中国大陆)   \n",
      "32                NaN   \n",
      "33             88分钟   \n",
      "34            110分钟   \n",
      "35             90分钟   \n",
      "36             85分钟   \n",
      "37             96分钟   \n",
      "38             82分钟   \n",
      "39            125分钟   \n",
      "40            128分钟   \n",
      "41                NaN   \n",
      "42                NaN   \n",
      "43            114分钟   \n",
      "44            117分钟   \n",
      "45                NaN   \n",
      "46            125分钟   \n",
      "47            107分钟   \n",
      "48                NaN   \n",
      "\n",
      "                                                         上映时间   \n",
      "0   2023-12-15(中国大陆)/2023-08-31(威尼斯国际电影节)/2023-09-...  \\\n",
      "1     2023-12-15(中国大陆)/2023-11-03(金鸡国产新片展)               \n",
      "2                                2023-12-16(中国大陆)               \n",
      "3               2023-12-16(中国大陆)/2023-04-14(日本)               \n",
      "4   2023-12-08(中国大陆)/2023-12-06(中国台湾)/2023-12-15(美...      \n",
      "5                                2023-11-25(中国大陆)               \n",
      "6                                2023-12-08(中国大陆)               \n",
      "7                                2023-12-08(中国大陆)               \n",
      "8                                2023-12-08(中国大陆)               \n",
      "9       2023-12-15(中国大陆)/2023-11-03(金鸡国际影展)               \n",
      "10         2023-11-17(美国/中国大陆)/2023-11-15(法国)               \n",
      "11                               2023-11-24(中国大陆)               \n",
      "12                               2023-12-01(中国大陆)               \n",
      "13  2023-10-21(中国大陆)/2023-05-20(戛纳电影节)/2023-10-11(...      \n",
      "14              2023-12-01(中国大陆)/2023-11-22(美国)               \n",
      "15              2023-11-25(中国大陆)/2023-08-04(日本)               \n",
      "16   2023-11-30(中国大陆)/2023-07-31(FIRST青年电影展)               \n",
      "17              2023-12-08(中国大陆)/2023-12-22(美国)               \n",
      "18              2023-11-24(中国大陆)/2023-11-22(美国)               \n",
      "19                               2023-11-24(中国大陆)               \n",
      "20              2023-11-17(中国大陆)/1992-07-18(日本)               \n",
      "21    2023-11-10(中国大陆)/2023-11-01(金鸡国产新片展)               \n",
      "22                               2023-09-28(中国大陆)               \n",
      "23    2023-12-01(中国大陆)/2023-10-14(平遥国际电影展)               \n",
      "24      2023-12-11(中国大陆)/2023-11-02(厦门金鸡影展)               \n",
      "25                               2023-12-01(中国大陆)               \n",
      "26                               2023-12-08(中国大陆)               \n",
      "27                               2023-10-27(中国大陆)               \n",
      "28    2023-11-17(中国大陆)/2023-06-10(上海国际电影节)               \n",
      "29    2023-12-08(中国大陆)/2022-11-04(重庆青年电影展)               \n",
      "30                               2023-11-24(中国大陆)               \n",
      "31              2023-03-24(中国大陆)/2022-11-11(日本)               \n",
      "32                               2023-11-11(中国大陆)               \n",
      "33                          2023-09-29(美国/中国大陆)               \n",
      "34   2023-10-20(中国大陆)/2023-07-24(FIRST青年电影展)               \n",
      "35   2023-11-16(中国大陆)/2022-07-30(FIRST青年电影展)               \n",
      "36                               2023-09-29(中国大陆)               \n",
      "37                               2023-09-28(中国大陆)               \n",
      "38                               2023-09-29(中国大陆)               \n",
      "39  2023-12-15(中国大陆)/2023-08-31(威尼斯国际电影节)/2023-09-...   \n",
      "40    2023-12-15(中国大陆)/2023-11-03(金鸡国产新片展)               \n",
      "41                               2023-12-16(中国大陆)               \n",
      "42              2023-12-16(中国大陆)/2023-04-14(日本)               \n",
      "43  2023-12-08(中国大陆)/2023-12-06(中国台湾)/2023-12-15(美...      \n",
      "44                               2023-11-25(中国大陆)               \n",
      "45                               2023-12-08(中国大陆)               \n",
      "46                               2023-12-08(中国大陆)               \n",
      "47                               2023-12-08(中国大陆)               \n",
      "48      2023-12-15(中国大陆)/2023-11-03(金鸡国际影展)               \n",
      "\n",
      "                                  类型           制片国家   \n",
      "0                                 剧情          法国 美国  \\\n",
      "1                            剧情/犯罪           中国大陆   \n",
      "2                  剧情/动作/悬疑/犯罪           中国大陆   \n",
      "3        动作/动画/悬疑/惊悚/犯罪/冒险               日本   \n",
      "4                  喜剧/歌舞/奇幻/冒险   美国 英国 加拿大   \n",
      "5                       剧情/悬疑/犯罪           中国大陆   \n",
      "6                       剧情/悬疑/犯罪           中国大陆   \n",
      "7                       剧情/悬疑/奇幻           中国大陆   \n",
      "8                            动作/犯罪  中国香港 中国大陆   \n",
      "9                                 剧情           中国大陆   \n",
      "10       剧情/动作/科幻/惊悚/战争/冒险               美国   \n",
      "11                           剧情/传记           中国大陆   \n",
      "12                                剧情           中国大陆   \n",
      "13                           剧情/犯罪           中国大陆   \n",
      "14       剧情/动作/传记/历史/战争/冒险          英国 美国   \n",
      "15                           喜剧/动画               日本   \n",
      "16                           剧情/犯罪           中国大陆   \n",
      "17            喜剧/动作/动画/奇幻/冒险   美国 法国 加拿大   \n",
      "18            喜剧/动画/歌舞/奇幻/冒险               美国   \n",
      "19                           剧情/悬疑           中国大陆   \n",
      "20                      喜剧/动画/冒险               日本   \n",
      "21                      剧情/喜剧/家庭           中国大陆   \n",
      "22                           喜剧/爱情           中国大陆   \n",
      "23                      剧情/悬疑/犯罪           中国大陆   \n",
      "24                                剧情           中国大陆   \n",
      "25                                爱情           中国大陆   \n",
      "26                           惊悚/恐怖           中国大陆   \n",
      "27                           剧情/喜剧           中国大陆   \n",
      "28                      剧情/传记/历史           中国大陆   \n",
      "29                           剧情/爱情           中国大陆   \n",
      "30                                爱情  中国大陆 中国香港   \n",
      "31                      爱情/动画/奇幻               日本   \n",
      "32                           动画/奇幻           中国大陆   \n",
      "33  剧情/喜剧/动作/科幻/动画/奇幻/冒险        美国 加拿大   \n",
      "34                           剧情/爱情           中国大陆   \n",
      "35                           剧情/儿童           中国大陆   \n",
      "36                 剧情/喜剧/动画/奇幻           中国大陆   \n",
      "37                 喜剧/科幻/动画/冒险           中国大陆   \n",
      "38                 喜剧/动画/奇幻/冒险           中国大陆   \n",
      "39                                剧情               美国   \n",
      "40                           剧情/犯罪               美国   \n",
      "41                 剧情/动作/悬疑/犯罪           中国大陆   \n",
      "42       动作/动画/悬疑/惊悚/犯罪/冒险           中国大陆   \n",
      "43                 喜剧/歌舞/奇幻/冒险  中国大陆 中国香港   \n",
      "44                      剧情/悬疑/犯罪           中国大陆   \n",
      "45                      剧情/悬疑/犯罪    中国大陆 加拿大   \n",
      "46                      剧情/悬疑/奇幻  中国香港 中国大陆   \n",
      "47                           动作/犯罪           中国大陆   \n",
      "48                                剧情           中国大陆   \n",
      "\n",
      "                           导演   \n",
      "0                     吕克·贝松  \\\n",
      "1                          戴墨   \n",
      "2                        马浴柯   \n",
      "3                        立川让   \n",
      "4                       保罗·金   \n",
      "5                        曹保平   \n",
      "6                          陈卓   \n",
      "7                        蔡耳朵   \n",
      "8                 林超贤 唐唯瀚   \n",
      "9                          虹影   \n",
      "10              弗朗西斯·劳伦斯   \n",
      "11                  郑大圣 杨瑾   \n",
      "12                       王小列   \n",
      "13                       魏书钧   \n",
      "14                雷德利·斯科特   \n",
      "15                       大根仁   \n",
      "16                       忻钰坤   \n",
      "17      本杰明·雷内 盖洛·霍姆西   \n",
      "18  克里斯·巴克 弗恩·维拉桑托恩   \n",
      "19                       高群书   \n",
      "20                       宫崎骏   \n",
      "21                       张大鹏   \n",
      "22                       田羽生   \n",
      "23                       郝飞环   \n",
      "24                         邢潇   \n",
      "25                  郑来志 阿猛   \n",
      "26                       毕鉴威   \n",
      "27                       王子昭   \n",
      "28                         苗月   \n",
      "29                         唐皓   \n",
      "30                       夏永康   \n",
      "31                       新海诚   \n",
      "32                          NaN   \n",
      "33                卡尔·布伦克尔   \n",
      "34                       哈智超   \n",
      "35                       拉华加   \n",
      "36                 文帝 陈朝意    \n",
      "37                        王超    \n",
      "38                          NaN   \n",
      "39                       温子仁   \n",
      "40                达米恩·查泽雷   \n",
      "41                         曹盾   \n",
      "42                         郝蕴   \n",
      "43                       关智耀   \n",
      "44                       董润年   \n",
      "45         肖恩·帕特里克·奥赖利   \n",
      "46                       庄文强   \n",
      "47                  陈小明 章攀   \n",
      "48                       冯小刚   \n",
      "\n",
      "                                                          演员   \n",
      "0   卡莱伯·兰德里·琼斯 / 克里斯托弗·邓汉 / 马里莎·贝伦森......  \\\n",
      "1                                  张译 / 李晨 / 魏晨            \n",
      "2                            张家辉 / 阮经天 / 王大陆            \n",
      "3                      高山南 / 林原惠美 / 山崎和佳奈            \n",
      "4         提莫西·查拉梅 / 奥利维娅·科尔曼 / 休·格兰特            \n",
      "5                                  黄渤 / 周迅 / 祖峰            \n",
      "6                            许光汉 / 张钧甯 / 惠英红            \n",
      "7                            章若楠 / 白宇帆 / 刘奕君            \n",
      "8                            张家辉 / 陈伟霆 / 梁洛施            \n",
      "9                                左航 / 冯家妹 / 白恩            \n",
      "10            汤姆·布莱斯 / 瑞秋·齐格勒 / 彼特·丁拉基            \n",
      "11                               海清 / 陈永胜 / 柴烨            \n",
      "12                               闫妮 / 谭松韵 / 吴京            \n",
      "13                         朱一龙 / 曾美慧孜 / 侯天来            \n",
      "14          华金·菲尼克斯 / 凡妮莎·柯比 / 塔哈·拉希姆            \n",
      "15                   小林由美子 / 楢桥美纪 / 森川智之            \n",
      "16                               周冬雨 / 宋洋 / 袁弘            \n",
      "17   库梅尔·南贾尼 / 伊丽莎白·班克斯 / 科甘-迈克尔·凯            \n",
      "18        阿丽亚娜·德博斯 / 克里斯·派恩 / 艾伦·图代克            \n",
      "19                             张译 / 黄志忠 / 郎月婷            \n",
      "20                 森山周一郎 / 加藤登纪子 / 冈村明美            \n",
      "21                             张译 / 潘斌龙 / 周依然            \n",
      "22                               韩庚 / 郑恺 / 于文文            \n",
      "23                               章宇 / 马吟吟 / 孙敏            \n",
      "24                                      李泰 / 周静波            \n",
      "25                             徐若晗 / 张开泰 / 秦奋            \n",
      "26                           邱意浓 / 丁圣霏 / 廖茉汐            \n",
      "27                           于和伟 / 郭麒麟 / 倪虹洁            \n",
      "28                             陈瑾 / 林永健 / 黄品沅            \n",
      "29                           蒋中炜 / 蔡雪晴 / 王若珊            \n",
      "30                                 张震 / 春夏 / 李现            \n",
      "31                     原菜乃华 / 松村北斗 / 深津绘里            \n",
      "32                                                NaN            \n",
      "33        麦肯娜·格瑞丝 / 塔拉吉·P·汉森 / 马尔赛·马丁            \n",
      "34                             金世佳 / 邱泽 / 张钧甯            \n",
      "35                     周拉多杰 / 朋毛样专 / 尕斗扎西            \n",
      "36                               王薇 / 藤新 / 傅晨阳            \n",
      "37                               梁晓强 / 巴赫 / 张东            \n",
      "38                                                NaN            \n",
      "39            杰森·莫玛 / 帕特里克·威尔森 / 安珀·赫德            \n",
      "40                瑞恩·高斯林 / 艾玛·斯通 / 约翰·传奇            \n",
      "41                                 章宇 / 窦骁 / 吕凉            \n",
      "42                                                NaN            \n",
      "43                           刘德华 / 林家栋 / 彭于晏            \n",
      "44                               大鹏 / 白客 / 庄达菲            \n",
      "45    朗·普尔曼 / 帕顿·奥斯瓦尔特 / 克里斯托弗·普卢默            \n",
      "46                           梁朝伟 / 刘德华 / 蔡卓妍            \n",
      "47                             屈楚萧 / 张佳宁 / 傅菁            \n",
      "48                                 葛优 / 舒淇 / 范伟            \n",
      "\n",
      "                                                                                               简介   \n",
      "0   被暴戾的父亲伤害、被囚禁在狗笼生活数年的道格（卡莱伯·兰德里·琼斯 饰），长大后只能与他的数...     \\\n",
      "1   刑侦大队队长程兵（张译 饰）带领的三大队在办理一起恶性案件的过程中导致嫌犯之一意外死亡，被判...    \n",
      "2   横跨黑白两道只手遮天的洪泰集团正值换选之际，一个神秘杀手陈安（张家辉 饰）却突然只身闯入这个...    \n",
      "3   故事发生在东京八丈岛近海，一座将全球各地警方监控相连接的高科技设施——“太平洋浮标”即将启动...       \n",
      "4   影片聚焦经典之作《查理和巧克力工厂》的主人公威利·旺卡（提莫西·查拉梅 饰），这位年轻且天赋...      \n",
      "5   老金（黄渤 饰）为供女儿金丽娜（周依然 饰）留学终日捕鱼，却换来女儿身中17刀离奇身亡的死讯...       \n",
      "6   贪婪无厌的警长郑威（许光汉 饰）得知富商之妻乔文娜（张钧甯 饰）蒙冤卷入一场密室谋杀案后主动...     \n",
      "7   深巷的尽头，一间“照明商店”长年亮着灯，神秘的老板（刘奕君 饰）细心擦拭着每一个灯泡，形形色...      \n",
      "8   缉毒总督察李振邦（张家辉 饰）和卧底警员江铭（陈伟霆 饰）秘密执行追捕毒贩计划，无奈遭遇险境...     \n",
      "9   影片根据虹影同名长篇小说改编，以重庆“山城”为地理背景，讲述了在1976年，12 岁少年窦小...            \n",
      "10  18岁的科里奥兰纳斯·斯诺（汤姆·布莱斯 饰）被选为第十届饥饿游戏的导师，与来自第十二区的贫...        \n",
      "11  这是一个坚毅的女人和一群刚刚毕业还很稚嫩的老师，带着一百多个女孩成功逆天改命的故事，是关于爱...   \n",
      "12  为了完成女儿李妍（谭松韵 饰）高三前的小小心愿，父亲李博宇（吴京 饰）决心带着女儿远赴阿勒泰...     \n",
      "13  影片改编自余华同名先锋代表作，看似是马哲（朱一龙 饰）在找寻凶案的真相，但故事没有答案，因为...    \n",
      "14  影片讲述了法国皇帝拿破仑（华金·菲尼克斯 Joaquin Phoenix 饰）从士兵到帝王波...                     \n",
      "15  故事讲述住在诺斯特拉达姆斯邻镇的努斯特拉达马斯留下了一个预言：“在 20 和 23 并列的年...            \n",
      "16                         热搜背后，真相究竟是什么？                                                 \n",
      "17  飞鸭马拉德一家向南迁徙前往热带的牙买加，却意外闯入疯狂大都市纽约？在紧张刺激的飞行之旅中，他...   \n",
      "18  在位于伊比利亚半岛外有一片梦幻之地，17岁的亚莎（阿丽亚娜·德博斯 Ariana DeBos...                   \n",
      "19  1940年，多方势力盘踞南京，特工金深水（张译 饰）终日周旋于日本军方、汪伪政府和军统之间，...        \n",
      "20  罗森是一战中意大利空军的王牌飞行员，但却被施了魔法变了一头猪。虽然变了猪，但罗森还是继续他的...   \n",
      "21  “石头”石振邦（张译 饰）和杨武（潘斌龙 饰）这对落魄兄弟经营五金店艰难维生，为追讨欠款，他...       \n",
      "22  孟云（韩庚 饰）和余飞（郑恺 饰）这对难兄难弟，在历经情场的洗礼之后，终于来到了谈婚论嫁的年...     \n",
      "23  平西镇位于西南某地，90年代乡镇治安由联防队维护，联防队长李立忠（章宇 饰）的父亲意外死亡，...      \n",
      "24  影片讲述了一个名叫仲肯的汉族男人，在藏区流浪多年，执着地想要成为格萨尔王传世故事的颂唱艺人“...    \n",
      "25  和你走到最后就是我最大的愿望。大大咧咧的夏末（徐若晗 饰）和温润内敛的初秋（张开泰 饰）见证...     \n",
      "26  影片讲述邱意浓饰演的美艳女医生误入诡宅后遇到的一系列灵异案件，晶女郎和民国恐怖元素双重 bu...      \n",
      "27  这是一个关于“儿子‘死’了，爸爸‘红’了”的荒诞故事。儿子坠楼“自杀”，父亲替儿子写了一封才...           \n",
      "28  影片记述了“敦煌女儿”，国家荣誉称号获得者樊锦诗坚守大漠，守护世界宝藏莫高窟的壮丽人生故事。...     \n",
      "29  情人节，对于陈良古（蒋中炜 饰）来说不过是无数个工作日的其中一天，避开对自己爱理不理的妻子，...    \n",
      "30  等一个人很久会有结果吗？等了初恋男友10年的林楚楚（春夏 饰），最终等来的是男友和别人的婚礼...      \n",
      "31  宁静的九州乡间小镇，生活着平凡的少女岩户铃芽（原菜乃华 配音）。这天上学路上，她邂逅了神秘的...    \n",
      "32  蓝玲市正在面临空前危机。汉堡军团在城市街头四处作乱，破坏街道和建筑，让市民们苦不堪言，但迷你...   \n",
      "33  一颗陨石从天而降，汪汪队基地惨遭破坏，因祸得福的是，汪汪队通过陨石获得了超能力！变成超能狗狗...   \n",
      "34  人生失意的B超师曹卢医（金世佳 饰）经人介绍来到了东山庭院疗养院工作。在这里，曹卢医看到了另...     \n",
      "35  影片讲述了一个青海高原藏区村落，少年多杰和姐姐格桑与奶奶一同生活。早年失去妈妈的姐弟俩渴望获...   \n",
      "36  陈塘关大战后哪吒和龙王三太子同归于尽，太乙真人将哪吒的仙体带回乾元山，等待时机将哪吒重生来帮...   \n",
      "37  最倒霉的特工贝肯熊，为了重获认可，偷偷混入火星任务，不料却闯进了火星里的神秘世界，和要抓捕的...   \n",
      "38  一夜之间，虎门武馆馆主惨遭毒手，少馆主乐乐也被神秘人物追杀，为报父仇和查清事情背后的真相，乐...   \n",
      "39  被暴戾的父亲伤害、被囚禁在狗笼生活数年的道格（卡莱伯·兰德里·琼斯 饰），长大后只能与他的数...      \n",
      "40  刑侦大队队长程兵（张译 饰）带领的三大队在办理一起恶性案件的过程中导致嫌犯之一意外死亡，被判...    \n",
      "41  横跨黑白两道只手遮天的洪泰集团正值换选之际，一个神秘杀手陈安（张家辉 饰）却突然只身闯入这个...    \n",
      "42  故事发生在东京八丈岛近海，一座将全球各地警方监控相连接的高科技设施——“太平洋浮标”即将启动...       \n",
      "43  影片聚焦经典之作《查理和巧克力工厂》的主人公威利·旺卡（提莫西·查拉梅 饰），这位年轻且天赋...      \n",
      "44  老金（黄渤 饰）为供女儿金丽娜（周依然 饰）留学终日捕鱼，却换来女儿身中17刀离奇身亡的死讯...       \n",
      "45  贪婪无厌的警长郑威（许光汉 饰）得知富商之妻乔文娜（张钧甯 饰）蒙冤卷入一场密室谋杀案后主动...     \n",
      "46  深巷的尽头，一间“照明商店”长年亮着灯，神秘的老板（刘奕君 饰）细心擦拭着每一个灯泡，形形色...      \n",
      "47  缉毒总督察李振邦（张家辉 饰）和卧底警员江铭（陈伟霆 饰）秘密执行追捕毒贩计划，无奈遭遇险境...     \n",
      "48  影片根据虹影同名长篇小说改编，以重庆“山城”为地理背景，讲述了在1976年，12 岁少年窦小...            \n",
      "\n",
      "    评价人数  \n",
      "0    21292.0  \n",
      "1    59951.0  \n",
      "2        NaN  \n",
      "3    36602.0  \n",
      "4    51017.0  \n",
      "5   268633.0  \n",
      "6    36452.0  \n",
      "7    29675.0  \n",
      "8    22957.0  \n",
      "9        NaN  \n",
      "10   52976.0  \n",
      "11       NaN  \n",
      "12   23244.0  \n",
      "13  288791.0  \n",
      "14   35688.0  \n",
      "15   22166.0  \n",
      "16   25981.0  \n",
      "17       NaN  \n",
      "18   17826.0  \n",
      "19   16917.0  \n",
      "20  250206.0  \n",
      "21   33612.0  \n",
      "22  102247.0  \n",
      "23    5150.0  \n",
      "24       NaN  \n",
      "25       NaN  \n",
      "26       NaN  \n",
      "27   36928.0  \n",
      "28    2449.0  \n",
      "29       NaN  \n",
      "30    1166.0  \n",
      "31  422227.0  \n",
      "32       NaN  \n",
      "33    5155.0  \n",
      "34    5123.0  \n",
      "35       NaN  \n",
      "36    4059.0  \n",
      "37    3610.0  \n",
      "38       NaN  \n",
      "39   21298.0  \n",
      "40   59964.0  \n",
      "41       NaN  \n",
      "42   36612.0  \n",
      "43   51019.0  \n",
      "44  268635.0  \n",
      "45   36453.0  \n",
      "46   29677.0  \n",
      "47   22958.0  \n",
      "48       NaN  \n"
     ]
    }
   ],
   "source": [
    "pd.set_option('display.unicode.east_asian_width', True)\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6391ba04",
   "metadata": {},
   "source": [
    "# Series对象\n",
    "\n",
    "**pandas. Series(data, index=index)**\n",
    "* data: 表示数据，支持列表、字典、numpy数组等\n",
    "* index: 表示行标签(索引)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac699d00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "4    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "tmp = pd.Series([1, 2, 3, 4, 5])\n",
    "print(tmp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31558807",
   "metadata": {},
   "source": [
    "## 手动设置索引\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f1b3af6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    1\n",
      "b    2\n",
      "c    3\n",
      "d    4\n",
      "e    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "tmp = pd.Series([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e'])\n",
    "print(tmp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff0ccd5b",
   "metadata": {},
   "source": [
    "### 位置索引\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "be59cc98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n"
     ]
    }
   ],
   "source": [
    "tmp = pd.Series([10, 20, 30, 40, 50])\n",
    "print(tmp[3])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9faf0c4a",
   "metadata": {},
   "source": [
    "### 标签索引\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a077a29d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "tmp = pd.Series([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'r', 'e'])\n",
    "print(tmp['r'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffbc2c00",
   "metadata": {},
   "source": [
    "## 切片索引\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "38d35ab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "dtype: int64\n",
      "q    3\n",
      "d    4\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "tmp = pd.Series([1, 2, 3, 4, 5])\n",
    "print(tmp[0:3])\n",
    "\n",
    "tmp = pd.Series([1, 2, 3, 4, 5], index=['a', 'b', 'q', 'd', 'e'])\n",
    "print(tmp['q':'d'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90538d17",
   "metadata": {},
   "source": [
    "## index属性 values属性\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "62f0b8ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['a', 'b', 'q', 'd', 'e'], dtype='object')\n",
      "[ 1  2 36  4  5]\n"
     ]
    }
   ],
   "source": [
    "tmp = pd.Series([1, 2, 36, 4, 5], index=['a', 'b', 'q', 'd', 'e'])\n",
    "print(tmp.index)\n",
    "print(tmp.values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0c86522",
   "metadata": {},
   "source": [
    "## Series常见属性\n",
    "\n",
    "| 属性 | 说明 |\n",
    "| ---- | --- |\n",
    "| Series.index | 获取行索引(行标签) |\n",
    "| Series.values | 返回包含数据的数组 |\n",
    "| Series.dtype | 返回基础的数据类型对象 |\n",
    "| Series.shape | 返回基础形状的元组 |\n",
    "| Series.size | 返回元素个数 |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d14349",
   "metadata": {},
   "source": [
    "# DataFrame对象\n",
    "\n",
    "**pandas. DataFrame(data, index, columns, dtype, copy)**\n",
    "* data: 表示数据，可以是ndarray, 列表, 字典等\n",
    "* index: 表示行标签(索引)\n",
    "* columns: 表示列标签\n",
    "* dtype: 每一列数据的数据类型，与Python的数据类型有区别\n",
    "* copy: 用于复制数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c467ed36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a  b  c\n",
      "1  1  2  3\n",
      "2  4  5  6\n",
      "3  7  8  9\n"
     ]
    }
   ],
   "source": [
    "tmp = pd.DataFrame(data=[[1, 2, 3], [4, 5, 6], [7, 8, 9]],\n",
    "                   index=[1, 2, 3],\n",
    "                   columns=['a', 'b', 'c'])\n",
    "print(tmp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e49392f3",
   "metadata": {},
   "source": [
    "## 数据类型比较\n",
    "\n",
    "| Pandas数据类型 | Python数据类型 |\n",
    "| ------------- | ------------- |\n",
    "| object | str |\n",
    "| int64 | int |\n",
    "| float64 | float |\n",
    "| bool | bool |\n",
    "| datetime64 | datetime64[ns] |\n",
    "| timedelta[ns] | NA |\n",
    "| category | NA |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1b0da24",
   "metadata": {},
   "source": [
    "## 重要属性\n",
    "\n",
    "| 属性 | 说明 | 举例 |\n",
    "| ---- | ---- | ---- |\n",
    "| values | 查看所有元素的值 | df.values |\n",
    "| dtypes | 查看所有元素的类型 | df.dtypes |\n",
    "| index | 查看所有行名(索引) | df.index |\n",
    "| columns | 查看所有列名 | df.columns |\n",
    "| T | 行列转换(转置) | df. T |\n",
    "| head | 查看前n条数据，默认5条 | df.head()<br/>df.head(10) |\n",
    "| tail | 查看后n条数据，默认5条 | df.tail()<br/>df.tail(10) |\n",
    "| shape | 查看行列数 | df.shape |\n",
    "| describe | 查看每列的统计汇总信息 | df.describe() |\n",
    "| count | 返回每一列中非空值的个数 | df.count() |\n",
    "| sum | 返回每一列的和，无法计算***会报错*** | df.sum() |\n",
    "| max | 返回每一列的最大值 | df.max() |\n",
    "| min | 返回每一列的最小值 | df.min() |\n",
    "| argmax | 返回某一列最大值所在的行索引 | df.argmax() |\n",
    "| argmin | 返回某一列最小值所在的行索引 | df.argmin() |\n",
    "| idxmax | 返回某一列最大值所在的列索引 | df.idxmax() |\n",
    "| idxmin | 返回某一列最小值所在的列索引 | df.idxmin() |\n",
    "| mean | 返回每一列的平均值 | df.mean() |\n",
    "| median | 返回每一列的中位数 | df.median() |\n",
    "| var | 返回每一列的方差 | df.var() |\n",
    "| std | 返回每一列的标准差 | df.std() |\n",
    "| isnull | 检查df中的空值，空值为True，非空为False，返回布尔类型数组 | df.isnull() |\n",
    "| notnull | 检查df中的空值，非空为True，空值为False，返回布尔类型数组 | df.notnull() |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf1c999c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['狗神' 'nowplaying' 8.3 4.5 '114分钟'\n",
      "  '2023-12-15(中国大陆)/2023-08-31(威尼斯国际电影节)/2023-09-27(法国)/2023-10-15(平遥国际电影展)'\n",
      "  '剧情' '法国 美国' '吕克·贝松' '卡莱伯·兰德里·琼斯 / 克里斯托弗·邓汉 / 马里莎·贝伦森'\n",
      "  '被暴戾的父亲伤害、被囚禁在狗笼生活数年的道格（卡莱伯·兰德里·琼斯 饰），长大后只能与他的数百只狗相互陪伴。然而命运对他的摧残从未停止，在经历了家庭、爱情、生存的一系列痛苦打击后，道格化身为最危险艳丽的“狗神”杀手，和他的狗群一起，向所有伤害过他的人发起了绝不饶恕的复仇与反抗......'\n",
      "  21292.0]\n",
      " ['三大队' 'nowplaying' 7.9 4.0 '132分钟'\n",
      "  '2023-12-15(中国大陆)/2023-11-03(金鸡国产新片展)' '剧情/犯罪' '中国大陆' '戴墨'\n",
      "  '张译 / 李晨 / 魏晨'\n",
      "  '刑侦大队队长程兵（张译 饰）带领的三大队在办理一起恶性案件的过程中导致嫌犯之一意外死亡，被判入狱。出狱后依然坚持以普通人身份追踪在逃嫌犯。'\n",
      "  59951.0]\n",
      " ['怒潮' 'nowplaying' nan nan '106分钟' '2023-12-16(中国大陆)' '剧情/动作/悬疑/犯罪'\n",
      "  '中国大陆' '马浴柯' '张家辉 / 阮经天 / 王大陆'\n",
      "  '横跨黑白两道只手遮天的洪泰集团正值换选之际，一个神秘杀手陈安（张家辉 饰）却突然只身闯入这个是非混乱的旋涡，搅得洪泰集团大乱。身处警察阵营的麦朗汶（阮经天 饰）和黑帮阵营的马文康（王大陆 饰）也盯上了他……各方势力伺机而动，谁才是幕后的操控者？一场生猛混战一触即发。'\n",
      "  nan]\n",
      " ['名侦探柯南：黑铁的鱼影' 'nowplaying' 6.7 3.5 '110分钟'\n",
      "  '2023-12-16(中国大陆)/2023-04-14(日本)' '动作/动画/悬疑/惊悚/犯罪/冒险' '日本' '立川让'\n",
      "  '高山南 / 林原惠美 / 山崎和佳奈'\n",
      "  '故事发生在东京八丈岛近海，一座将全球各地警方监控相连接的高科技设施——“太平洋浮标”即将启动。为了与欧洲刑警组织管辖的范围联网，来自世界各地的工程师聚集在这里。基于人脸识别的全新“黑科技”也正在测试中……'\n",
      "  36602.0]\n",
      " ['旺卡' 'nowplaying' 7.6 4.0 '116分钟'\n",
      "  '2023-12-08(中国大陆)/2023-12-06(中国台湾)/2023-12-15(美国)' '喜剧/歌舞/奇幻/冒险'\n",
      "  '美国 英国 加拿大' '保罗·金' '提莫西·查拉梅 / 奥利维娅·科尔曼 / 休·格兰特'\n",
      "  '影片聚焦经典之作《查理和巧克力工厂》的主人公威利·旺卡（提莫西·查拉梅 饰），这位年轻且天赋异禀的发明家、魔术师、巧克力制作师，是如何经历奇幻冒险，成长为观众挚爱的巧克力工厂掌门人的故事。'\n",
      "  51017.0]\n",
      " ['涉过愤怒的海' 'nowplaying' 7.5 4.0 '144分钟' '2023-11-25(中国大陆)' '剧情/悬疑/犯罪'\n",
      "  '中国大陆' '曹保平' '黄渤 / 周迅 / 祖峰'\n",
      "  '老金（黄渤 饰）为供女儿金丽娜（周依然 饰）留学终日捕鱼，却换来女儿身中17刀离奇身亡的死讯，最大嫌疑人则指向她的男友李苗苗（张宥浩 饰）。愤怒的老金踏上跨海寻仇之路，李苗苗却在母亲景岚（周迅 饰）的庇护下不断潜逃……这场人性迷局中，究竟谁能抵达爱与宽恕的彼岸？'\n",
      "  268633.0]\n",
      " ['瞒天过海' 'nowplaying' 6.3 3.5 '105分钟' '2023-12-08(中国大陆)' '剧情/悬疑/犯罪'\n",
      "  '中国大陆' '陈卓' '许光汉 / 张钧甯 / 惠英红'\n",
      "  '贪婪无厌的警长郑威（许光汉 饰）得知富商之妻乔文娜（张钧甯 饰）蒙冤卷入一场密室谋杀案后主动登门交易，索要巨额钱财帮她洗脱罪名。随着交易展开，竟意外牵连出另一起凶杀案，丈夫失踪的虹姐（惠英红 饰）自曝被强权欺压，走投无路的她向杀害丈夫的议员之子明浩（尹正 饰）绝望复仇……'\n",
      "  36452.0]\n",
      " ['照明商店' 'nowplaying' 6.5 3.5 '99分钟' '2023-12-08(中国大陆)' '剧情/悬疑/奇幻' '中国大陆'\n",
      "  '蔡耳朵' '章若楠 / 白宇帆 / 刘奕君'\n",
      "  '深巷的尽头，一间“照明商店”长年亮着灯，神秘的老板（刘奕君 饰）细心擦拭着每一个灯泡，形形色色的客人络绎不绝。护士许念（章若楠 饰）和男友郑满（白宇帆 饰）新搬进了商店附近的公寓。她渐渐发现，这里的每一个人，心底都有一个不可言说的秘密。暴风雨之夜，她走进了巷尾的照明商店，答案渐渐被揭开……'\n",
      "  29675.0]\n",
      " ['爆裂点' 'nowplaying' 6.5 3.5 '138分钟' '2023-12-08(中国大陆)' '动作/犯罪'\n",
      "  '中国香港 中国大陆' '林超贤 唐唯瀚' '张家辉 / 陈伟霆 / 梁洛施'\n",
      "  '缉毒总督察李振邦（张家辉 饰）和卧底警员江铭（陈伟霆 饰）秘密执行追捕毒贩计划，无奈遭遇险境，江铭被迫游走黑白边缘，逐渐失控。为坚守底线，昔日队友互生杀意。猜忌、愤怒、疯狂、失望……全员丧失理智，濒临崩溃边缘！'\n",
      "  22957.0]\n",
      " ['月光武士' 'nowplaying' nan nan '110分钟'\n",
      "  '2023-12-15(中国大陆)/2023-11-03(金鸡国际影展)' '剧情' '中国大陆' '虹影' '左航 / 冯家妹 / 白恩'\n",
      "  '影片根据虹影同名长篇小说改编，以重庆“山城”为地理背景，讲述了在1976年，12 岁少年窦小明因受伤进了医院，认识了对他照顾和关爱有加的护士佳惠。佳惠生得美貌，却嫁给当地混社会的青年钢哥，常常遭受家暴。窦小明得知此事后站出来打抱不平，从此改变了他们三人的命运。'\n",
      "  nan]\n",
      " ['饥饿游戏：鸣鸟与蛇之歌' 'nowplaying' 7.0 3.5 '158分钟'\n",
      "  '2023-11-17(美国/中国大陆)/2023-11-15(法国)' '剧情/动作/科幻/惊悚/战争/冒险' '美国'\n",
      "  '弗朗西斯·劳伦斯' '汤姆·布莱斯 / 瑞秋·齐格勒 / 彼特·丁拉基'\n",
      "  '18岁的科里奥兰纳斯·斯诺（汤姆·布莱斯 饰）被选为第十届饥饿游戏的导师，与来自第十二区的贫困女孩露西·格蕾·贝尔德（瑞秋·齐格勒 饰）成为了“生死相依”的命运共同体。竞技场上，这是一场关乎自由、食物，甚至是尊重与权利的生死角逐；竞技场外，不忍与善意使斯诺决定不顾一切帮助露西在这场游戏中赢得胜利......当活下去的欲望与所遵守的规则背道而驰，命运的齿轮紧抓入自己手中时，他又该如何抉择？'\n",
      "  52976.0]\n",
      " ['我本是高山' 'nowplaying' nan nan '131分钟' '2023-11-24(中国大陆)' '剧情/传记' '中国大陆'\n",
      "  '郑大圣 杨瑾' '海清 / 陈永胜 / 柴烨'\n",
      "  '这是一个坚毅的女人和一群刚刚毕业还很稚嫩的老师，带着一百多个女孩成功逆天改命的故事，是关于爱、青春、热血和命运……' nan]\n",
      " ['再见，李可乐' 'nowplaying' 6.7 3.5 '105分钟' '2023-12-01(中国大陆)' '剧情' '中国大陆'\n",
      "  '王小列' '闫妮 / 谭松韵 / 吴京'\n",
      "  '为了完成女儿李妍（谭松韵 饰）高三前的小小心愿，父亲李博宇（吴京 饰）决心带着女儿远赴阿勒泰滑雪。却不料李博宇遭遇意外，全力抢救后仍陷入脑死亡的处境，让为妻为母的潘雁秋（闫妮 饰）做出了一个女人最艰难的决定。当家渐渐失温，一只名为可乐的小狗带着倔强闯入了她们的生活，成为母女争执的焦点，也成为苦乐烟火的陪伴。如何在伤痛后放下悔与执？如何在成长中习得爱与勇？当往日的困境重现，她们的人生，又是否有无憾的选择？'\n",
      "  23244.0]\n",
      " ['河边的错误' 'nowplaying' 7.3 4.0 '102分钟'\n",
      "  '2023-10-21(中国大陆)/2023-05-20(戛纳电影节)/2023-10-11(平遥国际电影展)' '剧情/犯罪' '中国大陆'\n",
      "  '魏书钧' '朱一龙 / 曾美慧孜 / 侯天来'\n",
      "  '影片改编自余华同名先锋代表作，看似是马哲（朱一龙 饰）在找寻凶案的真相，但故事没有答案，因为命运的看法比我们更准确……' 288791.0]\n",
      " ['拿破仑' 'nowplaying' 6.5 3.5 '157分钟(中国大陆)'\n",
      "  '2023-12-01(中国大陆)/2023-11-22(美国)' '剧情/动作/传记/历史/战争/冒险' '英国 美国' '雷德利·斯科特'\n",
      "  '华金·菲尼克斯 / 凡妮莎·柯比 / 塔哈·拉希姆'\n",
      "  '影片讲述了法国皇帝拿破仑（华金·菲尼克斯 Joaquin Phoenix 饰）从士兵到帝王波澜壮阔的一生，展现了拿破仑非凡的军事才能和战略远见。并通过他与其唯一挚爱约瑟芬（凡妮莎·柯比 Vanessa Kirby 饰）之间复杂纠葛的传奇情史，揭露了拿破仑鲜为人知的内心世界。'\n",
      "  35688.0]\n",
      " ['蜡笔小新：新次元！超能力大决战' 'nowplaying' 8.1 4.0 '94分钟'\n",
      "  '2023-11-25(中国大陆)/2023-08-04(日本)' '喜剧/动画' '日本' '大根仁'\n",
      "  '小林由美子 / 楢桥美纪 / 森川智之'\n",
      "  '故事讲述住在诺斯特拉达姆斯邻镇的努斯特拉达马斯留下了一个预言：“在 20 和 23 并列的年份，天上会降下两道光芒。一道是黑暗之光，另一道是小小的白光……不久，黑暗之光就会拥有强大的力量，扰乱和平，给世界带来巨大的混乱。”'\n",
      "  22166.0]\n",
      " ['热搜' 'nowplaying' 6.3 3.5 '121分钟'\n",
      "  '2023-11-30(中国大陆)/2023-07-31(FIRST青年电影展)' '剧情/犯罪' '中国大陆' '忻钰坤'\n",
      "  '周冬雨 / 宋洋 / 袁弘' '热搜背后，真相究竟是什么？' 25981.0]\n",
      " ['飞鸭向前冲' 'nowplaying' nan nan '82分钟' '2023-12-08(中国大陆)/2023-12-22(美国)'\n",
      "  '喜剧/动作/动画/奇幻/冒险' '美国 法国 加拿大' '本杰明·雷内 盖洛·霍姆西'\n",
      "  '库梅尔·南贾尼 / 伊丽莎白·班克斯 / 科甘-迈克尔·凯'\n",
      "  '飞鸭马拉德一家向南迁徙前往热带的牙买加，却意外闯入疯狂大都市纽约？在紧张刺激的飞行之旅中，他们将如何化险为夷？' nan]\n",
      " ['星愿' 'nowplaying' 6.7 3.5 '95分钟' '2023-11-24(中国大陆)/2023-11-22(美国)'\n",
      "  '喜剧/动画/歌舞/奇幻/冒险' '美国' '克里斯·巴克 弗恩·维拉桑托恩' '阿丽亚娜·德博斯 / 克里斯·派恩 / 艾伦·图代克'\n",
      "  '在位于伊比利亚半岛外有一片梦幻之地，17岁的亚莎（阿丽亚娜·德博斯 Ariana DeBose 配音）和她的宠物山羊生活在被称为愿望王国的罗萨斯，人们从四面八方来到这里，向一位神奇的国王麦尼菲科（克里斯·派恩 Chris Pine 配音）表达他们的愿望，后者承诺有一天会满足他们最深的愿望，而只有他才能决定哪些愿望将在何时实现。机敏的的理想主义者阿莎许下了一个尤其强大的愿望，得到了宇宙力量的回应——一个被称为星星的充满无限能量的小球。阿莎和星星一同面临最强大的敌人，推翻麦尼菲科的统治以拯救她的社区，并证实当一个勇敢之人的意志与星星的魔力相结合后，奇妙的事情就会发生。'\n",
      "  17826.0]\n",
      " ['刀尖' 'nowplaying' 6.0 3.0 '121分钟' '2023-11-24(中国大陆)' '剧情/悬疑' '中国大陆'\n",
      "  '高群书' '张译 / 黄志忠 / 郎月婷'\n",
      "  '1940年，多方势力盘踞南京，特工金深水（张译 饰）终日周旋于日本军方、汪伪政府和军统之间，如同行走于刀尖一般，有着多重身份的他，在满目疮痍的南京城里小心翼翼地活着，隐藏着真实的自己，也寻找着自己乃至整个民族的生路。直到他遇到了和他一样心怀国仇家恨、却似乎比他更游刃有余的林婴婴（郎月婷 饰），在和她并肩作战与各方势力展开明争暗斗的过程中，于乱世中求生的金深水第一次开始真正地觉醒……'\n",
      "  16917.0]\n",
      " ['红猪' 'nowplaying' 8.6 4.5 '94分钟' '2023-11-17(中国大陆)/1992-07-18(日本)'\n",
      "  '喜剧/动画/冒险' '日本' '宫崎骏' '森山周一郎 / 加藤登纪子 / 冈村明美'\n",
      "  '罗森是一战中意大利空军的王牌飞行员，但却被施了魔法变了一头猪。虽然变了猪，但罗森还是继续他的老本行，成了一位赏金猎人，专门打击在亚地里亚海空域作恶的空中劫匪。红猪侠一时威名远扬，令劫匪们闻风丧胆。匪徒们为了对付罗森，请来了超级飞行员卡基斯。卡基斯在与罗森对决中趁他发动机故障将其击落。罗森把飞机送到老朋友比克罗处修理时，遇上了比克罗的孙女菲欧。菲欧帮罗森修好了飞机，两人一起再度翱翔蓝天对付空中劫匪。'\n",
      "  250206.0]\n",
      " ['无价之宝' 'nowplaying' 6.5 3.5 '132分钟'\n",
      "  '2023-11-10(中国大陆)/2023-11-01(金鸡国产新片展)' '剧情/喜剧/家庭' '中国大陆' '张大鹏'\n",
      "  '张译 / 潘斌龙 / 周依然'\n",
      "  '“石头”石振邦（张译 饰）和杨武（潘斌龙 饰）这对落魄兄弟经营五金店艰难维生，为追讨欠款，他们不得不暂时照看欠债人的孩子芊芊。两人在与芊芊的相处中从最初嫌弃“拖油瓶”竟潜移默化地转变成千方百计“为女儿战斗”，生活粗放的他们内心也变得越来越柔软……'\n",
      "  33612.0]\n",
      " ['前任4：英年早婚' 'nowplaying' 5.7 3.0 '129分钟' '2023-09-28(中国大陆)' '喜剧/爱情'\n",
      "  '中国大陆' '田羽生' '韩庚 / 郑恺 / 于文文'\n",
      "  '孟云（韩庚 饰）和余飞（郑恺 饰）这对难兄难弟，在历经情场的洗礼之后，终于来到了谈婚论嫁的年纪。正在经历“分手后遗症”的孟云，面对家人的催促，带着“为什么要结婚”的疑问，开始寻找真爱；而余飞和女友丁点（曾梦雪 饰）为了应对结婚后即将产生的各种情况，自创了“结婚冷静期”，将婚后生活提前预演。两兄弟开启一场全新的爱情挑战……'\n",
      "  102247.0]\n",
      " ['沉默笔录' 'nowplaying' 6.9 3.5 '91分钟'\n",
      "  '2023-12-01(中国大陆)/2023-10-14(平遥国际电影展)' '剧情/悬疑/犯罪' '中国大陆' '郝飞环'\n",
      "  '章宇 / 马吟吟 / 孙敏'\n",
      "  '平西镇位于西南某地，90年代乡镇治安由联防队维护，联防队长李立忠（章宇 饰）的父亲意外死亡，与此同时镇里发生诡异的连环杀狗案，李立忠带领队员通过蹩脚的探案手法进行调查，通过一系列荒诞行动，李立忠发现杀狗案竟与父亲的死亡有千丝万缕的关系。在那个野蛮生长的时代下，面对真相的李立忠只能以沉默作答。'\n",
      "  5150.0]\n",
      " ['仲肯' 'nowplaying' nan nan '87分钟' '2023-12-11(中国大陆)/2023-11-02(厦门金鸡影展)'\n",
      "  '剧情' '中国大陆' '邢潇' '李泰 / 周静波'\n",
      "  '影片讲述了一个名叫仲肯的汉族男人，在藏区流浪多年，执着地想要成为格萨尔王传世故事的颂唱艺人“仲肯”，但终因路途所遇人与事，渐渐放下执念，回归自我的故事。'\n",
      "  nan]\n",
      " ['不要走散好不好' 'nowplaying' nan nan '99分钟' '2023-12-01(中国大陆)' '爱情' '中国大陆'\n",
      "  '郑来志 阿猛' '徐若晗 / 张开泰 / 秦奋'\n",
      "  '和你走到最后就是我最大的愿望。大大咧咧的夏末（徐若晗 饰）和温润内敛的初秋（张开泰 饰）见证了彼此美好的青葱岁月，陪伴对方度过了很多难忘的瞬间，有青梅竹马的默契，有抵抗现实的勇气。他的爱意轰轰烈烈，她的喜欢坚定不移，或许真爱之路并不平坦，误解和错过一度让两个相爱的人走到分开的边缘。可是对的人，又怎么会走散呢？'\n",
      "  nan]\n",
      " ['诡摇铃' 'nowplaying' nan nan '85分钟' '2023-12-08(中国大陆)' '惊悚/恐怖' '中国大陆'\n",
      "  '毕鉴威' '邱意浓 / 丁圣霏 / 廖茉汐'\n",
      "  '影片讲述邱意浓饰演的美艳女医生误入诡宅后遇到的一系列灵异案件，晶女郎和民国恐怖元素双重 buff叠加，看点十足。' nan]\n",
      " ['二手杰作' 'nowplaying' 6.7 3.5 '114分钟' '2023-10-27(中国大陆)' '剧情/喜剧' '中国大陆'\n",
      "  '王子昭' '于和伟 / 郭麒麟 / 倪虹洁'\n",
      "  '这是一个关于“儿子‘死’了，爸爸‘红’了”的荒诞故事。儿子坠楼“自杀”，父亲替儿子写了一封才华横溢的代笔遗书，竟然一夜成名……'\n",
      "  36928.0]\n",
      " ['吾爱敦煌' 'nowplaying' 7.1 3.5 '101分钟'\n",
      "  '2023-11-17(中国大陆)/2023-06-10(上海国际电影节)' '剧情/传记/历史' '中国大陆' '苗月'\n",
      "  '陈瑾 / 林永健 / 黄品沅' '影片记述了“敦煌女儿”，国家荣誉称号获得者樊锦诗坚守大漠，守护世界宝藏莫高窟的壮丽人生故事。'\n",
      "  2449.0]\n",
      " ['最后一夜' 'nowplaying' nan nan '89分钟'\n",
      "  '2023-12-08(中国大陆)/2022-11-04(重庆青年电影展)' '剧情/爱情' '中国大陆' '唐皓'\n",
      "  '蒋中炜 / 蔡雪晴 / 王若珊'\n",
      "  '情人节，对于陈良古（蒋中炜 饰）来说不过是无数个工作日的其中一天，避开对自己爱理不理的妻子，躲到对自己关爱有加的情人家里享受片刻的温存，是他再平常不过的生活轨迹。然而今天，他却接二连三的遭遇意外，原来每个人都藏着一个冷暖自知的秘密，每个人都在茫然的摸索着生活的方向。车子在这喧嚣与宁静更迭交替的大都市里不断前行，却始终开不到目的地，正如他们在各自人生的地图上， 像个迷失方向的路盲。'\n",
      "  nan]\n",
      " ['靠近我一点' 'nowplaying' 4.1 2.0 '94分钟' '2023-11-24(中国大陆)' '爱情' '中国大陆 中国香港'\n",
      "  '夏永康' '张震 / 春夏 / 李现'\n",
      "  '等一个人很久会有结果吗？等了初恋男友10年的林楚楚（春夏 饰），最终等来的是男友和别人的婚礼。正当她对感情失去信心的时候，婚礼策划师张扬（张震 饰）和高富帅男神于可风（李现 饰）同时出现在楚楚的生活中，陪她走出了这段黑暗时光。'\n",
      "  1166.0]\n",
      " ['铃芽之旅' 'nowplaying' 7.2 4.0 '122分钟(中国大陆)'\n",
      "  '2023-03-24(中国大陆)/2022-11-11(日本)' '爱情/动画/奇幻' '日本' '新海诚'\n",
      "  '原菜乃华 / 松村北斗 / 深津绘里'\n",
      "  '宁静的九州乡间小镇，生活着平凡的少女岩户铃芽（原菜乃华 配音）。这天上学路上，她邂逅了神秘的白衣青年宗像草太（松村北斗 配音）。草太的言行举止引起了铃芽的好奇，她随着这个青年来到了一处废墟，并看到了一扇在水中央突兀伫立的门。懵懵懂懂，铃芽无意间解开了某个古老封印。在此之 后，一场地震突发，而偏偏只有铃芽才能看见地震前发生的诡异现象。随后这个惶恐的女孩才发现，草太原来是有着古老传承的闭门师，他巡游日本各地正是为了阻止更大的灾难发生。'\n",
      "  422227.0]\n",
      " ['迷你特工队之汉堡大作战' 'nowplaying' nan nan nan '2023-11-11(中国大陆)' '动画/奇幻'\n",
      "  '中国大陆' nan nan\n",
      "  '蓝玲市正在面临空前危机。汉堡军团在城市街头四处作乱，破坏街道和建筑，让市民们苦不堪言，但迷你特工队却爆发了激烈的内部矛盾。这一切是由于队长福乐偶然成为了汉堡店的超级模特，难以兼顾迷你特工队的集体任务。小米与福乐为此爆发了激烈的争吵，福乐宣告退出迷你特工队，与伙伴们不欢而散。汉堡店的老板金先生更是从中作梗：“我认为好朋友之间，就应该互相鼓励支持。”在危机之际，福乐终于悔悟，重新加入战斗，迷你特工队得以再度集结并肩作战。联手击败汉堡怪物队长后，整起事件背后真正的幕后黑手也终于出现在迷你特工队面前……'\n",
      "  nan]\n",
      " ['汪汪队立大功大电影2：超能大冒险' 'nowplaying' 7.5 4.0 '88分钟' '2023-09-29(美国/中国大陆)'\n",
      "  '剧情/喜剧/动作/科幻/动画/奇幻/冒险' '美国 加拿大' '卡尔·布伦克尔' '麦肯娜·格瑞丝 / 塔拉吉·P·汉森 / 马尔赛·马丁'\n",
      "  '一颗陨石从天而降，汪汪队基地惨遭破坏，因祸得福的是，汪汪队通过陨石获得了超能力！变成超能狗狗！能随心飞的天天、有飞毛腿的阿奇、是万人迷的灰灰、会霹雳火的毛毛、成大力士的小砾、不怕风浪的路马……莱德队长还给汪汪队升级了全新的装备，并招募汪汪预备队的三只小狗，共同保护冒险湾！但与此同时，一位邪恶的科学家也盯上了陨石中的超能力，她联合了汪汪队的死对头海丁那市长，试图夺取汪汪队的超能力，并让更多的陨石撞上地球！汪汪队集体出动，勇敢狗狗，不怕困难！汪汪队能否再次拯救世界？'\n",
      "  5155.0]\n",
      " ['最好的相遇' 'nowplaying' 6.3 3.5 '110分钟'\n",
      "  '2023-10-20(中国大陆)/2023-07-24(FIRST青年电影展)' '剧情/爱情' '中国大陆' '哈智超'\n",
      "  '金世佳 / 邱泽 / 张钧甯'\n",
      "  '人生失意的B超师曹卢医（金世佳 饰）经人介绍来到了东山庭院疗养院工作。在这里，曹卢医看到了另一个如世外桃源般不同寻常的世界，也结识了刘英俊（邱泽 饰）和吴燕子（张钧甯 饰）这一对特殊的恋人。他们笨拙却无比坚定的爱情慢慢改变了隐藏着秘密的曹卢医。而直到一场意外的降临，让所有人都陷入了艰难的抉择……'\n",
      "  5123.0]\n",
      " ['千里送鹤' 'nowplaying' nan nan '90分钟'\n",
      "  '2023-11-16(中国大陆)/2022-07-30(FIRST青年电影展)' '剧情/儿童' '中国大陆' '拉华加'\n",
      "  '周拉多杰 / 朋毛样专 / 尕斗扎西'\n",
      "  '影片讲述了一个青海高原藏区村落，少年多杰和姐姐格桑与奶奶一同生活。早年失去妈妈的姐弟俩渴望获父亲的陪伴，但父亲一直在外忙碌，很少回家。一天，姐弟俩偶遇一只受伤的小黑颈鹤，善良的他们将其带回家照顾。冬季将至，没有亲鸟的带领，小鹤无法完成迁徙，姐弟俩决定骑摩托送它前往云南越冬地。而父亲听闻孩子们擅自离家，也立刻南下寻找。这趟艰险的旅途，注定将是一家三口的成长之旅，促使他们重新审视亲情的涵义……'\n",
      "  nan]\n",
      " ['我是哪吒2之英雄归来' 'nowplaying' 2.4 1.5 '85分钟' '2023-09-29(中国大陆)'\n",
      "  '剧情/喜剧/动画/奇幻' '中国大陆' '文帝 陈朝意 ' '王薇 / 藤新 / 傅晨阳'\n",
      "  '陈塘关大战后哪吒和龙王三太子同归于尽，太乙真人将哪吒的仙体带回乾元山，等待时机将哪吒重生来帮助姜子牙封神伐纣。在重生的紧要关头哪吒却落入申公豹和东海龙王精心布置好的巨大阴谋之中。哪吒能否打破申公豹的魔咒？能否成为真正的英雄？哪吒艰难的重生之路即将开启，英雄归来之战一触即发……'\n",
      "  4059.0]\n",
      " ['贝肯熊：火星任务' 'nowplaying' 7.2 3.5 '96分钟' '2023-09-28(中国大陆)' '喜剧/科幻/动画/冒险'\n",
      "  '中国大陆' '王超 ' '梁晓强 / 巴赫 / 张东'\n",
      "  '最倒霉的特工贝肯熊，为了重获认可，偷偷混入火星任务，不料却闯进了火星里的神秘世界，和要抓捕的火星怪物成为了朋友。在特工荣誉和友情之间，他将面临一场前所未有的考验。'\n",
      "  3610.0]\n",
      " ['功夫王之萌虎上山' 'nowplaying' nan nan '82分钟' '2023-09-29(中国大陆)' '喜剧/动画/奇幻/冒险'\n",
      "  '中国大陆' nan nan\n",
      "  '一夜之间，虎门武馆馆主惨遭毒手，少馆主乐乐也被神秘人物追杀，为报父仇和查清事情背后的真相，乐乐带着小伙伴毅然踏上前往功夫山的征程，前方的路会是一马平川的坦途吗？'\n",
      "  nan]\n",
      " ['海王2：失落的王国' 'upcoming' nan nan '125分钟'\n",
      "  '2023-12-15(中国大陆)/2023-08-31(威尼斯国际电影节)/2023-09-27(法国)/2023-10-15(平遥国际电影展)'\n",
      "  '剧情' '美国' '温子仁' '杰森·莫玛 / 帕特里克·威尔森 / 安珀·赫德'\n",
      "  '被暴戾的父亲伤害、被囚禁在狗笼生活数年的道格（卡莱伯·兰德里·琼斯 饰），长大后只能与他的数百只狗相互陪伴。然而命运对他的摧残从未停止，在经历了家庭、爱情、生存的一系列痛苦打击后，道格化身为最危险艳丽的“狗神”杀手，和他的狗群一起，向所有伤害过他的人发起了绝不饶恕的复仇与反抗......'\n",
      "  21298.0]\n",
      " ['爱乐之城' 'upcoming' nan nan '128分钟'\n",
      "  '2023-12-15(中国大陆)/2023-11-03(金鸡国产新片展)' '剧情/犯罪' '美国' '达米恩·查泽雷'\n",
      "  '瑞恩·高斯林 / 艾玛·斯通 / 约翰·传奇'\n",
      "  '刑侦大队队长程兵（张译 饰）带领的三大队在办理一起恶性案件的过程中导致嫌犯之一意外死亡，被判入狱。出狱后依然坚持以普通人身份追踪在逃嫌犯。'\n",
      "  59964.0]\n",
      " ['敦煌英雄' 'upcoming' nan nan nan '2023-12-16(中国大陆)' '剧情/动作/悬疑/犯罪' '中国大陆'\n",
      "  '曹盾' '章宇 / 窦骁 / 吕凉'\n",
      "  '横跨黑白两道只手遮天的洪泰集团正值换选之际，一个神秘杀手陈安（张家辉 饰）却突然只身闯入这个是非混乱的旋涡，搅得洪泰集团大乱。身处警察阵营的麦朗汶（阮经天 饰）和黑帮阵营的马文康（王大陆 饰）也盯上了他……各方势力伺机而动，谁才是幕后的操控者？一场生猛混战一触即发。'\n",
      "  nan]\n",
      " ['人民万岁' 'upcoming' nan nan nan '2023-12-16(中国大陆)/2023-04-14(日本)'\n",
      "  '动作/动画/悬疑/惊悚/犯罪/冒险' '中国大陆' '郝蕴' nan\n",
      "  '故事发生在东京八丈岛近海，一座将全球各地警方监控相连接的高科技设施——“太平洋浮标”即将启动。为了与欧洲刑警组织管辖的范围联网，来自世界各地的工程师聚集在这里。基于人脸识别的全新“黑科技”也正在测试中……'\n",
      "  36612.0]\n",
      " ['潜行' 'upcoming' nan nan '114分钟'\n",
      "  '2023-12-08(中国大陆)/2023-12-06(中国台湾)/2023-12-15(美国)' '喜剧/歌舞/奇幻/冒险'\n",
      "  '中国大陆 中国香港' '关智耀' '刘德华 / 林家栋 / 彭于晏'\n",
      "  '影片聚焦经典之作《查理和巧克力工厂》的主人公威利·旺卡（提莫西·查拉梅 饰），这位年轻且天赋异禀的发明家、魔术师、巧克力制作师，是如何经历奇幻冒险，成长为观众挚爱的巧克力工厂掌门人的故事。'\n",
      "  51019.0]\n",
      " ['年会不能停！' 'upcoming' nan nan '117分钟' '2023-11-25(中国大陆)' '剧情/悬疑/犯罪'\n",
      "  '中国大陆' '董润年' '大鹏 / 白客 / 庄达菲'\n",
      "  '老金（黄渤 饰）为供女儿金丽娜（周依然 饰）留学终日捕鱼，却换来女儿身中17刀离奇身亡的死讯，最大嫌疑人则指向她的男友李苗苗（张宥浩 饰）。愤怒的老金踏上跨海寻仇之路，李苗苗却在母亲景岚（周迅 饰）的庇护下不断潜逃……这场人性迷局中，究竟谁能抵达爱与宽恕的彼岸？'\n",
      "  268635.0]\n",
      " ['金色面具英雄' 'upcoming' nan nan nan '2023-12-08(中国大陆)' '剧情/悬疑/犯罪'\n",
      "  '中国大陆 加拿大' '肖恩·帕特里克·奥赖利' '朗·普尔曼 / 帕顿·奥斯瓦尔特 / 克里斯托弗·普卢默'\n",
      "  '贪婪无厌的警长郑威（许光汉 饰）得知富商之妻乔文娜（张钧甯 饰）蒙冤卷入一场密室谋杀案后主动登门交易，索要巨额钱财帮她洗脱罪名。随着交易展开，竟意外牵连出另一起凶杀案，丈夫失踪的虹姐（惠英红 饰）自曝被强权欺压，走投无路的她向杀害丈夫的议员之子明浩（尹正 饰）绝望复仇……'\n",
      "  36453.0]\n",
      " ['金手指' 'upcoming' nan nan '125分钟' '2023-12-08(中国大陆)' '剧情/悬疑/奇幻'\n",
      "  '中国香港 中国大陆' '庄文强' '梁朝伟 / 刘德华 / 蔡卓妍'\n",
      "  '深巷的尽头，一间“照明商店”长年亮着灯，神秘的老板（刘奕君 饰）细心擦拭着每一个灯泡，形形色色的客人络绎不绝。护士许念（章若楠 饰）和男友郑满（白宇帆 饰）新搬进了商店附近的公寓。她渐渐发现，这里的每一个人，心底都有一个不可言说的秘密。暴风雨之夜，她走进了巷尾的照明商店，答案渐渐被揭开……'\n",
      "  29677.0]\n",
      " ['一闪一闪亮星星' 'upcoming' nan nan '107分钟' '2023-12-08(中国大陆)' '动作/犯罪' '中国大陆'\n",
      "  '陈小明 章攀' '屈楚萧 / 张佳宁 / 傅菁'\n",
      "  '缉毒总督察李振邦（张家辉 饰）和卧底警员江铭（陈伟霆 饰）秘密执行追捕毒贩计划，无奈遭遇险境，江铭被迫游走黑白边缘，逐渐失控。为坚守底线，昔日队友互生杀意。猜忌、愤怒、疯狂、失望……全员丧失理智，濒临崩溃边缘！'\n",
      "  22958.0]\n",
      " ['非诚勿扰3' 'upcoming' nan nan nan '2023-12-15(中国大陆)/2023-11-03(金鸡国际影展)'\n",
      "  '剧情' '中国大陆' '冯小刚' '葛优 / 舒淇 / 范伟'\n",
      "  '影片根据虹影同名长篇小说改编，以重庆“山城”为地理背景，讲述了在1976年，12 岁少年窦小明因受伤进了医院，认识了对他照顾和关爱有加的护士佳惠。佳惠生得美貌，却嫁给当地混社会的青年钢哥，常常遭受家暴。窦小明得知此事后站出来打抱不平，从此改变了他们三人的命运。'\n",
      "  nan]]\n",
      "电影名       object\n",
      "状态         object\n",
      "评分        float64\n",
      "星级        float64\n",
      "时长         object\n",
      "上映时间     object\n",
      "类型         object\n",
      "制片国家     object\n",
      "导演         object\n",
      "演员         object\n",
      "简介         object\n",
      "评价人数    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.values)\n",
    "# print(df.values[0:3])\n",
    "print(df.dtypes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "547dd826",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=49, step=1)\n",
      "Index(['电影名', '状态', '评分', '星级', '时长', '上映时间', '类型',\n",
      "       '制片国家', '导演', '演员', '简介', '评价人数'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.index)\n",
    "# print(type(df.index))\n",
    "print(df.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7c7d6902",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                    0    \n",
      "电影名                                                 狗神                                             \\\n",
      "状态                                             nowplaying                                              \n",
      "评分                                                    8.3                                              \n",
      "星级                                                    4.5                                              \n",
      "时长                                                114分钟                                              \n",
      "上映时间  2023-12-15(中国大陆)/2023-08-31(威尼斯国际电影节)/2023-09-...                                  \n",
      "类型                                                   剧情                                              \n",
      "制片国家                                          法国 美国                                              \n",
      "导演                                              吕克·贝松                                              \n",
      "演员      卡莱伯·兰德里·琼斯 / 克里斯托弗·邓汉 / 马里莎·贝伦森......                                     \n",
      "简介      被暴戾的父亲伤害、被囚禁在狗笼生活数年的道格（卡莱伯·兰德里·琼斯 饰），长大后只能与他的数...   \n",
      "评价人数                                            21292.0                                              \n",
      "\n",
      "                                                                                                      1    \n",
      "电影名                                               三大队                                               \\\n",
      "状态                                             nowplaying                                                \n",
      "评分                                                    7.9                                                \n",
      "星级                                                    4.0                                                \n",
      "时长                                                132分钟                                                \n",
      "上映时间    2023-12-15(中国大陆)/2023-11-03(金鸡国产新片展)                                                \n",
      "类型                                              剧情/犯罪                                                \n",
      "制片国家                                           中国大陆                                                \n",
      "导演                                                   戴墨                                                \n",
      "演员                                     张译 / 李晨 / 魏晨                                                \n",
      "简介      刑侦大队队长程兵（张译 饰）带领的三大队在办理一起恶性案件的过程中导致嫌犯之一意外死亡，被判...   \n",
      "评价人数                                            59951.0                                                \n",
      "\n",
      "                                                                                                      2    \n",
      "电影名                                                 怒潮                                               \\\n",
      "状态                                             nowplaying                                                \n",
      "评分                                                    NaN                                                \n",
      "星级                                                    NaN                                                \n",
      "时长                                                106分钟                                                \n",
      "上映时间                               2023-12-16(中国大陆)                                                \n",
      "类型                                    剧情/动作/悬疑/犯罪                                                \n",
      "制片国家                                           中国大陆                                                \n",
      "导演                                                 马浴柯                                                \n",
      "演员                               张家辉 / 阮经天 / 王大陆                                                \n",
      "简介      横跨黑白两道只手遮天的洪泰集团正值换选之际，一个神秘杀手陈安（张家辉 饰）却突然只身闯入这个...   \n",
      "评价人数                                                NaN                                                \n",
      "\n",
      "                                                                                                   3    \n",
      "电影名                               名侦探柯南：黑铁的鱼影                                            \\\n",
      "状态                                             nowplaying                                             \n",
      "评分                                                    6.7                                             \n",
      "星级                                                    3.5                                             \n",
      "时长                                                110分钟                                             \n",
      "上映时间              2023-12-16(中国大陆)/2023-04-14(日本)                                             \n",
      "类型                          动作/动画/悬疑/惊悚/犯罪/冒险                                             \n",
      "制片国家                                               日本                                             \n",
      "导演                                                 立川让                                             \n",
      "演员                         高山南 / 林原惠美 / 山崎和佳奈                                             \n",
      "简介      故事发生在东京八丈岛近海，一座将全球各地警方监控相连接的高科技设施——“太平洋浮标”即将启动...   \n",
      "评价人数                                            36602.0                                             \n",
      "\n",
      "                                                                                                    4    \n",
      "电影名                                                 旺卡                                             \\\n",
      "状态                                             nowplaying                                              \n",
      "评分                                                    7.6                                              \n",
      "星级                                                    4.0                                              \n",
      "时长                                                116分钟                                              \n",
      "上映时间  2023-12-08(中国大陆)/2023-12-06(中国台湾)/2023-12-15(美...                                     \n",
      "类型                                    喜剧/歌舞/奇幻/冒险                                              \n",
      "制片国家                                   美国 英国 加拿大                                              \n",
      "导演                                                保罗·金                                              \n",
      "演员            提莫西·查拉梅 / 奥利维娅·科尔曼 / 休·格兰特                                              \n",
      "简介      影片聚焦经典之作《查理和巧克力工厂》的主人公威利·旺卡（提莫西·查拉梅 饰），这位年轻且天赋...   \n",
      "评价人数                                            51017.0                                              \n",
      "\n",
      "                                                                                                   5    \n",
      "电影名                                         涉过愤怒的海                                            \\\n",
      "状态                                             nowplaying                                             \n",
      "评分                                                    7.5                                             \n",
      "星级                                                    4.0                                             \n",
      "时长                                                144分钟                                             \n",
      "上映时间                               2023-11-25(中国大陆)                                             \n",
      "类型                                         剧情/悬疑/犯罪                                             \n",
      "制片国家                                           中国大陆                                             \n",
      "导演                                                 曹保平                                             \n",
      "演员                                     黄渤 / 周迅 / 祖峰                                             \n",
      "简介      老金（黄渤 饰）为供女儿金丽娜（周依然 饰）留学终日捕鱼，却换来女儿身中17刀离奇身亡的死讯...   \n",
      "评价人数                                           268633.0                                             \n",
      "\n",
      "                                                                                                     6    \n",
      "电影名                                             瞒天过海                                              \\\n",
      "状态                                             nowplaying                                               \n",
      "评分                                                    6.3                                               \n",
      "星级                                                    3.5                                               \n",
      "时长                                                105分钟                                               \n",
      "上映时间                               2023-12-08(中国大陆)                                               \n",
      "类型                                         剧情/悬疑/犯罪                                               \n",
      "制片国家                                           中国大陆                                               \n",
      "导演                                                   陈卓                                               \n",
      "演员                               许光汉 / 张钧甯 / 惠英红                                               \n",
      "简介      贪婪无厌的警长郑威（许光汉 饰）得知富商之妻乔文娜（张钧甯 饰）蒙冤卷入一场密室谋杀案后主动...   \n",
      "评价人数                                            36452.0                                               \n",
      "\n",
      "                                                                                                    7    \n",
      "电影名                                             照明商店                                             \\\n",
      "状态                                             nowplaying                                              \n",
      "评分                                                    6.5                                              \n",
      "星级                                                    3.5                                              \n",
      "时长                                                 99分钟                                              \n",
      "上映时间                               2023-12-08(中国大陆)                                              \n",
      "类型                                         剧情/悬疑/奇幻                                              \n",
      "制片国家                                           中国大陆                                              \n",
      "导演                                                 蔡耳朵                                              \n",
      "演员                               章若楠 / 白宇帆 / 刘奕君                                              \n",
      "简介      深巷的尽头，一间“照明商店”长年亮着灯，神秘的老板（刘奕君 饰）细心擦拭着每一个灯泡，形形色...   \n",
      "评价人数                                            29675.0                                              \n",
      "\n",
      "                                                                                                     8    \n",
      "电影名                                               爆裂点                                              \\\n",
      "状态                                             nowplaying                                               \n",
      "评分                                                    6.5                                               \n",
      "星级                                                    3.5                                               \n",
      "时长                                                138分钟                                               \n",
      "上映时间                               2023-12-08(中国大陆)                                               \n",
      "类型                                              动作/犯罪                                               \n",
      "制片国家                                  中国香港 中国大陆                                               \n",
      "导演                                          林超贤 唐唯瀚                                               \n",
      "演员                               张家辉 / 陈伟霆 / 梁洛施                                               \n",
      "简介      缉毒总督察李振邦（张家辉 饰）和卧底警员江铭（陈伟霆 饰）秘密执行追捕毒贩计划，无奈遭遇险境...   \n",
      "评价人数                                            22957.0                                               \n",
      "\n",
      "                                                                                              9    \n",
      "电影名                                             月光武士                                       \\\n",
      "状态                                             nowplaying                                        \n",
      "评分                                                    NaN                                        \n",
      "星级                                                    NaN                                        \n",
      "时长                                                110分钟                                        \n",
      "上映时间      2023-12-15(中国大陆)/2023-11-03(金鸡国际影展)                                        \n",
      "类型                                                   剧情                                        \n",
      "制片国家                                           中国大陆                                        \n",
      "导演                                                   虹影                                        \n",
      "演员                                   左航 / 冯家妹 / 白恩                                        \n",
      "简介      影片根据虹影同名长篇小说改编，以重庆“山城”为地理背景，讲述了在1976年，12 岁少年窦小...   \n",
      "评价人数                                                NaN                                        \n",
      "\n",
      "          ...   \n",
      "电影名    ...  \\\n",
      "状态      ...   \n",
      "评分      ...   \n",
      "星级      ...   \n",
      "时长      ...   \n",
      "上映时间  ...   \n",
      "类型      ...   \n",
      "制片国家  ...   \n",
      "导演      ...   \n",
      "演员      ...   \n",
      "简介      ...   \n",
      "评价人数  ...   \n",
      "\n",
      "                                                                                                    39   \n",
      "电影名                                    海王2：失落的王国                                             \\\n",
      "状态                                               upcoming                                              \n",
      "评分                                                    NaN                                              \n",
      "星级                                                    NaN                                              \n",
      "时长                                                125分钟                                              \n",
      "上映时间  2023-12-15(中国大陆)/2023-08-31(威尼斯国际电影节)/2023-09-...                                  \n",
      "类型                                                   剧情                                              \n",
      "制片国家                                               美国                                              \n",
      "导演                                                 温子仁                                              \n",
      "演员                杰森·莫玛 / 帕特里克·威尔森 / 安珀·赫德                                              \n",
      "简介      被暴戾的父亲伤害、被囚禁在狗笼生活数年的道格（卡莱伯·兰德里·琼斯 饰），长大后只能与他的数...   \n",
      "评价人数                                            21298.0                                              \n",
      "\n",
      "                                                                                                      40   \n",
      "电影名                                             爱乐之城                                               \\\n",
      "状态                                               upcoming                                                \n",
      "评分                                                    NaN                                                \n",
      "星级                                                    NaN                                                \n",
      "时长                                                128分钟                                                \n",
      "上映时间    2023-12-15(中国大陆)/2023-11-03(金鸡国产新片展)                                                \n",
      "类型                                              剧情/犯罪                                                \n",
      "制片国家                                               美国                                                \n",
      "导演                                          达米恩·查泽雷                                                \n",
      "演员                    瑞恩·高斯林 / 艾玛·斯通 / 约翰·传奇                                                \n",
      "简介      刑侦大队队长程兵（张译 饰）带领的三大队在办理一起恶性案件的过程中导致嫌犯之一意外死亡，被判...   \n",
      "评价人数                                            59964.0                                                \n",
      "\n",
      "                                                                                                      41   \n",
      "电影名                                             敦煌英雄                                               \\\n",
      "状态                                               upcoming                                                \n",
      "评分                                                    NaN                                                \n",
      "星级                                                    NaN                                                \n",
      "时长                                                    NaN                                                \n",
      "上映时间                               2023-12-16(中国大陆)                                                \n",
      "类型                                    剧情/动作/悬疑/犯罪                                                \n",
      "制片国家                                           中国大陆                                                \n",
      "导演                                                   曹盾                                                \n",
      "演员                                     章宇 / 窦骁 / 吕凉                                                \n",
      "简介      横跨黑白两道只手遮天的洪泰集团正值换选之际，一个神秘杀手陈安（张家辉 饰）却突然只身闯入这个...   \n",
      "评价人数                                                NaN                                                \n",
      "\n",
      "                                                                                                   42   \n",
      "电影名                                             人民万岁                                            \\\n",
      "状态                                               upcoming                                             \n",
      "评分                                                    NaN                                             \n",
      "星级                                                    NaN                                             \n",
      "时长                                                    NaN                                             \n",
      "上映时间              2023-12-16(中国大陆)/2023-04-14(日本)                                             \n",
      "类型                          动作/动画/悬疑/惊悚/犯罪/冒险                                             \n",
      "制片国家                                           中国大陆                                             \n",
      "导演                                                   郝蕴                                             \n",
      "演员                                                    NaN                                             \n",
      "简介      故事发生在东京八丈岛近海，一座将全球各地警方监控相连接的高科技设施——“太平洋浮标”即将启动...   \n",
      "评价人数                                            36612.0                                             \n",
      "\n",
      "                                                                                                    43   \n",
      "电影名                                                 潜行                                             \\\n",
      "状态                                               upcoming                                              \n",
      "评分                                                    NaN                                              \n",
      "星级                                                    NaN                                              \n",
      "时长                                                114分钟                                              \n",
      "上映时间  2023-12-08(中国大陆)/2023-12-06(中国台湾)/2023-12-15(美...                                     \n",
      "类型                                    喜剧/歌舞/奇幻/冒险                                              \n",
      "制片国家                                  中国大陆 中国香港                                              \n",
      "导演                                                 关智耀                                              \n",
      "演员                               刘德华 / 林家栋 / 彭于晏                                              \n",
      "简介      影片聚焦经典之作《查理和巧克力工厂》的主人公威利·旺卡（提莫西·查拉梅 饰），这位年轻且天赋...   \n",
      "评价人数                                            51019.0                                              \n",
      "\n",
      "                                                                                                   44   \n",
      "电影名                                         年会不能停！                                            \\\n",
      "状态                                               upcoming                                             \n",
      "评分                                                    NaN                                             \n",
      "星级                                                    NaN                                             \n",
      "时长                                                117分钟                                             \n",
      "上映时间                               2023-11-25(中国大陆)                                             \n",
      "类型                                         剧情/悬疑/犯罪                                             \n",
      "制片国家                                           中国大陆                                             \n",
      "导演                                                 董润年                                             \n",
      "演员                                   大鹏 / 白客 / 庄达菲                                             \n",
      "简介      老金（黄渤 饰）为供女儿金丽娜（周依然 饰）留学终日捕鱼，却换来女儿身中17刀离奇身亡的死讯...   \n",
      "评价人数                                           268635.0                                             \n",
      "\n",
      "                                                                                                     45   \n",
      "电影名                                         金色面具英雄                                              \\\n",
      "状态                                               upcoming                                               \n",
      "评分                                                    NaN                                               \n",
      "星级                                                    NaN                                               \n",
      "时长                                                    NaN                                               \n",
      "上映时间                               2023-12-08(中国大陆)                                               \n",
      "类型                                         剧情/悬疑/犯罪                                               \n",
      "制片国家                                    中国大陆 加拿大                                               \n",
      "导演                                   肖恩·帕特里克·奥赖利                                               \n",
      "演员        朗·普尔曼 / 帕顿·奥斯瓦尔特 / 克里斯托弗·普卢默                                               \n",
      "简介      贪婪无厌的警长郑威（许光汉 饰）得知富商之妻乔文娜（张钧甯 饰）蒙冤卷入一场密室谋杀案后主动...   \n",
      "评价人数                                            36453.0                                               \n",
      "\n",
      "                                                                                                    46   \n",
      "电影名                                               金手指                                             \\\n",
      "状态                                               upcoming                                              \n",
      "评分                                                    NaN                                              \n",
      "星级                                                    NaN                                              \n",
      "时长                                                125分钟                                              \n",
      "上映时间                               2023-12-08(中国大陆)                                              \n",
      "类型                                         剧情/悬疑/奇幻                                              \n",
      "制片国家                                  中国香港 中国大陆                                              \n",
      "导演                                                 庄文强                                              \n",
      "演员                               梁朝伟 / 刘德华 / 蔡卓妍                                              \n",
      "简介      深巷的尽头，一间“照明商店”长年亮着灯，神秘的老板（刘奕君 饰）细心擦拭着每一个灯泡，形形色...   \n",
      "评价人数                                            29677.0                                              \n",
      "\n",
      "                                                                                                     47   \n",
      "电影名                                       一闪一闪亮星星                                              \\\n",
      "状态                                               upcoming                                               \n",
      "评分                                                    NaN                                               \n",
      "星级                                                    NaN                                               \n",
      "时长                                                107分钟                                               \n",
      "上映时间                               2023-12-08(中国大陆)                                               \n",
      "类型                                              动作/犯罪                                               \n",
      "制片国家                                           中国大陆                                               \n",
      "导演                                            陈小明 章攀                                               \n",
      "演员                                 屈楚萧 / 张佳宁 / 傅菁                                               \n",
      "简介      缉毒总督察李振邦（张家辉 饰）和卧底警员江铭（陈伟霆 饰）秘密执行追捕毒贩计划，无奈遭遇险境...   \n",
      "评价人数                                            22958.0                                               \n",
      "\n",
      "                                                                                              48  \n",
      "电影名                                            非诚勿扰3                                       \n",
      "状态                                               upcoming                                       \n",
      "评分                                                    NaN                                       \n",
      "星级                                                    NaN                                       \n",
      "时长                                                    NaN                                       \n",
      "上映时间      2023-12-15(中国大陆)/2023-11-03(金鸡国际影展)                                       \n",
      "类型                                                   剧情                                       \n",
      "制片国家                                           中国大陆                                       \n",
      "导演                                                 冯小刚                                       \n",
      "演员                                     葛优 / 舒淇 / 范伟                                       \n",
      "简介      影片根据虹影同名长篇小说改编，以重庆“山城”为地理背景，讲述了在1976年，12 岁少年窦小...  \n",
      "评价人数                                                NaN                                       \n",
      "\n",
      "[12 rows x 49 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df.T)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "59ebfb5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   电影名        状态  评分  星级     时长   \n",
      "0                    狗神  nowplaying   8.3   4.5  114分钟  \\\n",
      "1                  三大队  nowplaying   7.9   4.0  132分钟   \n",
      "2                    怒潮  nowplaying   NaN   NaN  106分钟   \n",
      "3  名侦探柯南：黑铁的鱼影  nowplaying   6.7   3.5  110分钟   \n",
      "4                    旺卡  nowplaying   7.6   4.0  116分钟   \n",
      "\n",
      "                                                        上映时间   \n",
      "0  2023-12-15(中国大陆)/2023-08-31(威尼斯国际电影节)/2023-09-...  \\\n",
      "1    2023-12-15(中国大陆)/2023-11-03(金鸡国产新片展)               \n",
      "2                               2023-12-16(中国大陆)               \n",
      "3              2023-12-16(中国大陆)/2023-04-14(日本)               \n",
      "4  2023-12-08(中国大陆)/2023-12-06(中国台湾)/2023-12-15(美...      \n",
      "\n",
      "                            类型          制片国家       导演   \n",
      "0                           剧情         法国 美国  吕克·贝松  \\\n",
      "1                      剧情/犯罪          中国大陆       戴墨   \n",
      "2            剧情/动作/悬疑/犯罪          中国大陆     马浴柯   \n",
      "3  动作/动画/悬疑/惊悚/犯罪/冒险              日本     立川让   \n",
      "4            喜剧/歌舞/奇幻/冒险  美国 英国 加拿大    保罗·金   \n",
      "\n",
      "                                                         演员   \n",
      "0  卡莱伯·兰德里·琼斯 / 克里斯托弗·邓汉 / 马里莎·贝伦森......  \\\n",
      "1                                 张译 / 李晨 / 魏晨            \n",
      "2                           张家辉 / 阮经天 / 王大陆            \n",
      "3                     高山南 / 林原惠美 / 山崎和佳奈            \n",
      "4        提莫西·查拉梅 / 奥利维娅·科尔曼 / 休·格兰特            \n",
      "\n",
      "                                                                                             简介   \n",
      "0  被暴戾的父亲伤害、被囚禁在狗笼生活数年的道格（卡莱伯·兰德里·琼斯 饰），长大后只能与他的数...    \\\n",
      "1  刑侦大队队长程兵（张译 饰）带领的三大队在办理一起恶性案件的过程中导致嫌犯之一意外死亡，被判...   \n",
      "2  横跨黑白两道只手遮天的洪泰集团正值换选之际，一个神秘杀手陈安（张家辉 饰）却突然只身闯入这个...   \n",
      "3  故事发生在东京八丈岛近海，一座将全球各地警方监控相连接的高科技设施——“太平洋浮标”即将启动...      \n",
      "4  影片聚焦经典之作《查理和巧克力工厂》的主人公威利·旺卡（提莫西·查拉梅 饰），这位年轻且天赋...     \n",
      "\n",
      "   评价人数  \n",
      "0   21292.0  \n",
      "1   59951.0  \n",
      "2       NaN  \n",
      "3   36602.0  \n",
      "4   51017.0  \n",
      "   电影名        状态  评分  星级     时长   \n",
      "0    狗神  nowplaying   8.3   4.5  114分钟  \\\n",
      "1  三大队  nowplaying   7.9   4.0  132分钟   \n",
      "\n",
      "                                                        上映时间       类型   \n",
      "0  2023-12-15(中国大陆)/2023-08-31(威尼斯国际电影节)/2023-09-...       剧情  \\\n",
      "1    2023-12-15(中国大陆)/2023-11-03(金鸡国产新片展)              剧情/犯罪   \n",
      "\n",
      "    制片国家       导演   \n",
      "0  法国 美国  吕克·贝松  \\\n",
      "1   中国大陆       戴墨   \n",
      "\n",
      "                                                         演员   \n",
      "0  卡莱伯·兰德里·琼斯 / 克里斯托弗·邓汉 / 马里莎·贝伦森......  \\\n",
      "1                                 张译 / 李晨 / 魏晨            \n",
      "\n",
      "                                                                                             简介   \n",
      "0  被暴戾的父亲伤害、被囚禁在狗笼生活数年的道格（卡莱伯·兰德里·琼斯 饰），长大后只能与他的数...    \\\n",
      "1  刑侦大队队长程兵（张译 饰）带领的三大队在办理一起恶性案件的过程中导致嫌犯之一意外死亡，被判...   \n",
      "\n",
      "   评价人数  \n",
      "0   21292.0  \n",
      "1   59951.0  \n"
     ]
    }
   ],
   "source": [
    "print(df.head())\n",
    "print(df.head(2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "127a0927",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            电影名      状态  评分  星级     时长   \n",
      "44    年会不能停！  upcoming   NaN   NaN  117分钟  \\\n",
      "45    金色面具英雄  upcoming   NaN   NaN      NaN   \n",
      "46          金手指  upcoming   NaN   NaN  125分钟   \n",
      "47  一闪一闪亮星星  upcoming   NaN   NaN  107分钟   \n",
      "48       非诚勿扰3  upcoming   NaN   NaN      NaN   \n",
      "\n",
      "                                         上映时间            类型   \n",
      "44                           2023-11-25(中国大陆)  剧情/悬疑/犯罪  \\\n",
      "45                           2023-12-08(中国大陆)  剧情/悬疑/犯罪   \n",
      "46                           2023-12-08(中国大陆)  剧情/悬疑/奇幻   \n",
      "47                           2023-12-08(中国大陆)       动作/犯罪   \n",
      "48  2023-12-15(中国大陆)/2023-11-03(金鸡国际影展)            剧情   \n",
      "\n",
      "             制片国家                  导演   \n",
      "44           中国大陆                董润年  \\\n",
      "45    中国大陆 加拿大  肖恩·帕特里克·奥赖利   \n",
      "46  中国香港 中国大陆                庄文强   \n",
      "47           中国大陆           陈小明 章攀   \n",
      "48           中国大陆                冯小刚   \n",
      "\n",
      "                                               演员   \n",
      "44                             大鹏 / 白客 / 庄达菲  \\\n",
      "45  朗·普尔曼 / 帕顿·奥斯瓦尔特 / 克里斯托弗·普卢默   \n",
      "46                         梁朝伟 / 刘德华 / 蔡卓妍   \n",
      "47                           屈楚萧 / 张佳宁 / 傅菁   \n",
      "48                               葛优 / 舒淇 / 范伟   \n",
      "\n",
      "                                                                                             简介   \n",
      "44  老金（黄渤 饰）为供女儿金丽娜（周依然 饰）留学终日捕鱼，却换来女儿身中17刀离奇身亡的死讯...    \\\n",
      "45  贪婪无厌的警长郑威（许光汉 饰）得知富商之妻乔文娜（张钧甯 饰）蒙冤卷入一场密室谋杀案后主动...   \n",
      "46  深巷的尽头，一间“照明商店”长年亮着灯，神秘的老板（刘奕君 饰）细心擦拭着每一个灯泡，形形色...    \n",
      "47  缉毒总督察李振邦（张家辉 饰）和卧底警员江铭（陈伟霆 饰）秘密执行追捕毒贩计划，无奈遭遇险境...   \n",
      "48  影片根据虹影同名长篇小说改编，以重庆“山城”为地理背景，讲述了在1976年，12 岁少年窦小...          \n",
      "\n",
      "    评价人数  \n",
      "44  268635.0  \n",
      "45   36453.0  \n",
      "46   29677.0  \n",
      "47   22958.0  \n",
      "48       NaN  \n",
      "            电影名      状态  评分  星级     时长   \n",
      "47  一闪一闪亮星星  upcoming   NaN   NaN  107分钟  \\\n",
      "48       非诚勿扰3  upcoming   NaN   NaN      NaN   \n",
      "\n",
      "                                         上映时间       类型  制片国家   \n",
      "47                           2023-12-08(中国大陆)  动作/犯罪  中国大陆  \\\n",
      "48  2023-12-15(中国大陆)/2023-11-03(金鸡国际影展)       剧情  中国大陆   \n",
      "\n",
      "           导演                    演员   \n",
      "47  陈小明 章攀  屈楚萧 / 张佳宁 / 傅菁  \\\n",
      "48       冯小刚      葛优 / 舒淇 / 范伟   \n",
      "\n",
      "                                                                                             简介   \n",
      "47  缉毒总督察李振邦（张家辉 饰）和卧底警员江铭（陈伟霆 饰）秘密执行追捕毒贩计划，无奈遭遇险境...  \\\n",
      "48  影片根据虹影同名长篇小说改编，以重庆“山城”为地理背景，讲述了在1976年，12 岁少年窦小...          \n",
      "\n",
      "    评价人数  \n",
      "47   22958.0  \n",
      "48       NaN  \n"
     ]
    }
   ],
   "source": [
    "print(df.tail())\n",
    "print(df.tail(2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "50ad535e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49, 12)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "216b9923",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            评分       星级       评价人数\n",
      "count  28.000000  28.000000      36.000000\n",
      "mean    6.717857   3.535714   66908.777778\n",
      "std     1.219913   0.622548   99764.503864\n",
      "min     2.400000   1.500000    1166.000000\n",
      "25%     6.450000   3.500000   20425.500000\n",
      "50%     6.700000   3.500000   31644.500000\n",
      "75%     7.350000   4.000000   51508.250000\n",
      "max     8.600000   4.500000  422227.000000\n"
     ]
    }
   ],
   "source": [
    "print(df.describe())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1a43a508",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "电影名      49\n",
      "状态        49\n",
      "评分        28\n",
      "星级        28\n",
      "时长        44\n",
      "上映时间    49\n",
      "类型        49\n",
      "制片国家    49\n",
      "导演        47\n",
      "演员        46\n",
      "简介        49\n",
      "评价人数    36\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.count())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0423d4c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188.1\n",
      "8.6\n",
      "2.4\n"
     ]
    }
   ],
   "source": [
    "print(df['评分'].sum())\n",
    "print(df['评分'].max())\n",
    "print(df['评分'].min())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "71565179",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31\n",
      "30\n",
      "31\n",
      "30\n"
     ]
    }
   ],
   "source": [
    "print(df['评价人数'].argmax())\n",
    "print(df['评价人数'].argmin())\n",
    "print(df['评价人数'].idxmax())\n",
    "print(df['评价人数'].idxmin())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "47cf2493",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.7178571428571425\n",
      "6.7\n",
      "1.488187830687831\n",
      "1.2199130422648292\n"
     ]
    }
   ],
   "source": [
    "print(df['评分'].mean())\n",
    "print(df['评分'].median())\n",
    "print(df['评分'].var())\n",
    "print(df['评分'].std())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "372fb469",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    电影名   状态   评分   星级   时长  上映时间   类型  制片国家   导演   \n",
      "0    False  False  False  False  False     False  False     False  False  \\\n",
      "1    False  False  False  False  False     False  False     False  False   \n",
      "2    False  False   True   True  False     False  False     False  False   \n",
      "3    False  False  False  False  False     False  False     False  False   \n",
      "4    False  False  False  False  False     False  False     False  False   \n",
      "5    False  False  False  False  False     False  False     False  False   \n",
      "6    False  False  False  False  False     False  False     False  False   \n",
      "7    False  False  False  False  False     False  False     False  False   \n",
      "8    False  False  False  False  False     False  False     False  False   \n",
      "9    False  False   True   True  False     False  False     False  False   \n",
      "10   False  False  False  False  False     False  False     False  False   \n",
      "11   False  False   True   True  False     False  False     False  False   \n",
      "12   False  False  False  False  False     False  False     False  False   \n",
      "13   False  False  False  False  False     False  False     False  False   \n",
      "14   False  False  False  False  False     False  False     False  False   \n",
      "15   False  False  False  False  False     False  False     False  False   \n",
      "16   False  False  False  False  False     False  False     False  False   \n",
      "17   False  False   True   True  False     False  False     False  False   \n",
      "18   False  False  False  False  False     False  False     False  False   \n",
      "19   False  False  False  False  False     False  False     False  False   \n",
      "20   False  False  False  False  False     False  False     False  False   \n",
      "21   False  False  False  False  False     False  False     False  False   \n",
      "22   False  False  False  False  False     False  False     False  False   \n",
      "23   False  False  False  False  False     False  False     False  False   \n",
      "24   False  False   True   True  False     False  False     False  False   \n",
      "25   False  False   True   True  False     False  False     False  False   \n",
      "26   False  False   True   True  False     False  False     False  False   \n",
      "27   False  False  False  False  False     False  False     False  False   \n",
      "28   False  False  False  False  False     False  False     False  False   \n",
      "29   False  False   True   True  False     False  False     False  False   \n",
      "30   False  False  False  False  False     False  False     False  False   \n",
      "31   False  False  False  False  False     False  False     False  False   \n",
      "32   False  False   True   True   True     False  False     False   True   \n",
      "33   False  False  False  False  False     False  False     False  False   \n",
      "34   False  False  False  False  False     False  False     False  False   \n",
      "35   False  False   True   True  False     False  False     False  False   \n",
      "36   False  False  False  False  False     False  False     False  False   \n",
      "37   False  False  False  False  False     False  False     False  False   \n",
      "38   False  False   True   True  False     False  False     False   True   \n",
      "39   False  False   True   True  False     False  False     False  False   \n",
      "40   False  False   True   True  False     False  False     False  False   \n",
      "41   False  False   True   True   True     False  False     False  False   \n",
      "42   False  False   True   True   True     False  False     False  False   \n",
      "43   False  False   True   True  False     False  False     False  False   \n",
      "44   False  False   True   True  False     False  False     False  False   \n",
      "45   False  False   True   True   True     False  False     False  False   \n",
      "46   False  False   True   True  False     False  False     False  False   \n",
      "47   False  False   True   True  False     False  False     False  False   \n",
      "48   False  False   True   True   True     False  False     False  False   \n",
      "\n",
      "     演员   简介  评价人数  \n",
      "0   False  False     False  \n",
      "1   False  False     False  \n",
      "2   False  False      True  \n",
      "3   False  False     False  \n",
      "4   False  False     False  \n",
      "5   False  False     False  \n",
      "6   False  False     False  \n",
      "7   False  False     False  \n",
      "8   False  False     False  \n",
      "9   False  False      True  \n",
      "10  False  False     False  \n",
      "11  False  False      True  \n",
      "12  False  False     False  \n",
      "13  False  False     False  \n",
      "14  False  False     False  \n",
      "15  False  False     False  \n",
      "16  False  False     False  \n",
      "17  False  False      True  \n",
      "18  False  False     False  \n",
      "19  False  False     False  \n",
      "20  False  False     False  \n",
      "21  False  False     False  \n",
      "22  False  False     False  \n",
      "23  False  False     False  \n",
      "24  False  False      True  \n",
      "25  False  False      True  \n",
      "26  False  False      True  \n",
      "27  False  False     False  \n",
      "28  False  False     False  \n",
      "29  False  False      True  \n",
      "30  False  False     False  \n",
      "31  False  False     False  \n",
      "32   True  False      True  \n",
      "33  False  False     False  \n",
      "34  False  False     False  \n",
      "35  False  False      True  \n",
      "36  False  False     False  \n",
      "37  False  False     False  \n",
      "38   True  False      True  \n",
      "39  False  False     False  \n",
      "40  False  False     False  \n",
      "41  False  False      True  \n",
      "42   True  False     False  \n",
      "43  False  False     False  \n",
      "44  False  False     False  \n",
      "45  False  False     False  \n",
      "46  False  False     False  \n",
      "47  False  False     False  \n",
      "48  False  False      True  \n",
      "    电影名  状态   评分   星级   时长  上映时间  类型  制片国家   导演   演员   \n",
      "0     True  True   True   True   True      True  True      True   True   True  \\\n",
      "1     True  True   True   True   True      True  True      True   True   True   \n",
      "2     True  True  False  False   True      True  True      True   True   True   \n",
      "3     True  True   True   True   True      True  True      True   True   True   \n",
      "4     True  True   True   True   True      True  True      True   True   True   \n",
      "5     True  True   True   True   True      True  True      True   True   True   \n",
      "6     True  True   True   True   True      True  True      True   True   True   \n",
      "7     True  True   True   True   True      True  True      True   True   True   \n",
      "8     True  True   True   True   True      True  True      True   True   True   \n",
      "9     True  True  False  False   True      True  True      True   True   True   \n",
      "10    True  True   True   True   True      True  True      True   True   True   \n",
      "11    True  True  False  False   True      True  True      True   True   True   \n",
      "12    True  True   True   True   True      True  True      True   True   True   \n",
      "13    True  True   True   True   True      True  True      True   True   True   \n",
      "14    True  True   True   True   True      True  True      True   True   True   \n",
      "15    True  True   True   True   True      True  True      True   True   True   \n",
      "16    True  True   True   True   True      True  True      True   True   True   \n",
      "17    True  True  False  False   True      True  True      True   True   True   \n",
      "18    True  True   True   True   True      True  True      True   True   True   \n",
      "19    True  True   True   True   True      True  True      True   True   True   \n",
      "20    True  True   True   True   True      True  True      True   True   True   \n",
      "21    True  True   True   True   True      True  True      True   True   True   \n",
      "22    True  True   True   True   True      True  True      True   True   True   \n",
      "23    True  True   True   True   True      True  True      True   True   True   \n",
      "24    True  True  False  False   True      True  True      True   True   True   \n",
      "25    True  True  False  False   True      True  True      True   True   True   \n",
      "26    True  True  False  False   True      True  True      True   True   True   \n",
      "27    True  True   True   True   True      True  True      True   True   True   \n",
      "28    True  True   True   True   True      True  True      True   True   True   \n",
      "29    True  True  False  False   True      True  True      True   True   True   \n",
      "30    True  True   True   True   True      True  True      True   True   True   \n",
      "31    True  True   True   True   True      True  True      True   True   True   \n",
      "32    True  True  False  False  False      True  True      True  False  False   \n",
      "33    True  True   True   True   True      True  True      True   True   True   \n",
      "34    True  True   True   True   True      True  True      True   True   True   \n",
      "35    True  True  False  False   True      True  True      True   True   True   \n",
      "36    True  True   True   True   True      True  True      True   True   True   \n",
      "37    True  True   True   True   True      True  True      True   True   True   \n",
      "38    True  True  False  False   True      True  True      True  False  False   \n",
      "39    True  True  False  False   True      True  True      True   True   True   \n",
      "40    True  True  False  False   True      True  True      True   True   True   \n",
      "41    True  True  False  False  False      True  True      True   True   True   \n",
      "42    True  True  False  False  False      True  True      True   True  False   \n",
      "43    True  True  False  False   True      True  True      True   True   True   \n",
      "44    True  True  False  False   True      True  True      True   True   True   \n",
      "45    True  True  False  False  False      True  True      True   True   True   \n",
      "46    True  True  False  False   True      True  True      True   True   True   \n",
      "47    True  True  False  False   True      True  True      True   True   True   \n",
      "48    True  True  False  False  False      True  True      True   True   True   \n",
      "\n",
      "    简介  评价人数  \n",
      "0   True      True  \n",
      "1   True      True  \n",
      "2   True     False  \n",
      "3   True      True  \n",
      "4   True      True  \n",
      "5   True      True  \n",
      "6   True      True  \n",
      "7   True      True  \n",
      "8   True      True  \n",
      "9   True     False  \n",
      "10  True      True  \n",
      "11  True     False  \n",
      "12  True      True  \n",
      "13  True      True  \n",
      "14  True      True  \n",
      "15  True      True  \n",
      "16  True      True  \n",
      "17  True     False  \n",
      "18  True      True  \n",
      "19  True      True  \n",
      "20  True      True  \n",
      "21  True      True  \n",
      "22  True      True  \n",
      "23  True      True  \n",
      "24  True     False  \n",
      "25  True     False  \n",
      "26  True     False  \n",
      "27  True      True  \n",
      "28  True      True  \n",
      "29  True     False  \n",
      "30  True      True  \n",
      "31  True      True  \n",
      "32  True     False  \n",
      "33  True      True  \n",
      "34  True      True  \n",
      "35  True     False  \n",
      "36  True      True  \n",
      "37  True      True  \n",
      "38  True     False  \n",
      "39  True      True  \n",
      "40  True      True  \n",
      "41  True     False  \n",
      "42  True      True  \n",
      "43  True      True  \n",
      "44  True      True  \n",
      "45  True      True  \n",
      "46  True      True  \n",
      "47  True      True  \n",
      "48  True     False  \n"
     ]
    }
   ],
   "source": [
    "print(df.isnull())\n",
    "print(df.notnull())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6178b375",
   "metadata": {},
   "source": [
    "## 导入数据\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e207f98d",
   "metadata": {},
   "source": [
    "### 导入excel\n",
    "\n",
    "**pandas.read_excel(io, sheet_name, header)**\n",
    "* io: 文件路径或文件对象\n",
    "* sheet_name: 工作表，值可以是None、字符串、整数或列表，默认值为0\n",
    "    - sheet_name=0: 第一个Sheet页的数据作为DataFrame对象\n",
    "    - sheet_name='Sheet1': 名为'Sheet1'的页的数据作为DataFrame对象\n",
    "    - sheet_name=[0,1,'Sheet2']: 第一个，第二个，以及名为'Sheet2'的页的数据作为DataFrame对象\n",
    "* header: 指定作为列名的行，默认值为0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "777fd6f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     序号        学号    姓名                  学院    年级              专业   \n",
      "0       1  2212080001  吕博宇  计算机与信息工程学院  2022级  计算机科学与技术  \\\n",
      "1       2  2212080069    周瑞  计算机与信息工程学院  2022级  计算机科学与技术   \n",
      "2       3  1916020148  熊英杰  计算机与信息工程学院  2022级  计算机科学与技术   \n",
      "3       4  2212080072  孙刚强  计算机与信息工程学院  2022级  计算机科学与技术   \n",
      "4       5  2212080047  吴永贵  计算机与信息工程学院  2022级  计算机科学与技术   \n",
      "..    ...         ...     ...                   ...     ...               ...   \n",
      "394   395  2310120054  胡天姿  计算机与信息工程学院  2023级          计算机类   \n",
      "395   396  2125050318    王可              软件学院  2021级          软件工程   \n",
      "396   397  2212080153  黄景浩  计算机与信息工程学院  2022级  计算机科学与技术   \n",
      "397   398  2212080160  魏文博  计算机与信息工程学院  2022级  计算机科学与技术   \n",
      "398   399  2212080156  贾晨琦  计算机与信息工程学院  2022级  计算机科学与技术   \n",
      "\n",
      "    性别           参赛分类           考场  \n",
      "0     男  C/C++程序设计竞赛  计算机大楼201  \n",
      "1     男  C/C++程序设计竞赛  计算机大楼201  \n",
      "2     男  C/C++程序设计竞赛  计算机大楼201  \n",
      "3     男  C/C++程序设计竞赛  计算机大楼201  \n",
      "4     男  C/C++程序设计竞赛  计算机大楼201  \n",
      "..   ...                ...            ...  \n",
      "394   女  C/C++程序设计竞赛  计算机大楼608  \n",
      "395   男  C/C++程序设计竞赛  计算机大楼605  \n",
      "396   男  C/C++程序设计竞赛  计算机大楼605  \n",
      "397   男  C/C++程序设计竞赛  计算机大楼605  \n",
      "398   男  C/C++程序设计竞赛  计算机大楼605  \n",
      "\n",
      "[399 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "tmp = pd.read_excel('./data/C++名单.xlsx', sheet_name='C++395人', header=1)\n",
    "print(tmp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80c9c2a",
   "metadata": {},
   "source": [
    "### 导入csv\n",
    "\n",
    "**pandas.read_csv(filepath, sep, header, encoding)**\n",
    "* filepath: 文件路径\n",
    "* sep: 指定分隔符，默认为逗号\n",
    "* header: 指定作为列名的行，默认值为0\n",
    "* encoding: 指定字符集，默认为'utf-8'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "30d83ac8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             电影名        状态  评分  星级   \n",
      "0                              狗神  nowplaying   8.3   4.5  \\\n",
      "1                            三大队  nowplaying   7.9   4.0   \n",
      "2                              怒潮  nowplaying   NaN   NaN   \n",
      "3            名侦探柯南：黑铁的鱼影  nowplaying   6.7   3.5   \n",
      "4                              旺卡  nowplaying   7.6   4.0   \n",
      "5                      涉过愤怒的海  nowplaying   7.5   4.0   \n",
      "6                          瞒天过海  nowplaying   6.3   3.5   \n",
      "7                          照明商店  nowplaying   6.5   3.5   \n",
      "8                            爆裂点  nowplaying   6.5   3.5   \n",
      "9                          月光武士  nowplaying   NaN   NaN   \n",
      "10           饥饿游戏：鸣鸟与蛇之歌  nowplaying   7.0   3.5   \n",
      "11                       我本是高山  nowplaying   NaN   NaN   \n",
      "12                     再见，李可乐  nowplaying   6.7   3.5   \n",
      "13                       河边的错误  nowplaying   7.3   4.0   \n",
      "14                           拿破仑  nowplaying   6.5   3.5   \n",
      "15   蜡笔小新：新次元！超能力大决战  nowplaying   8.1   4.0   \n",
      "16                             热搜  nowplaying   6.3   3.5   \n",
      "17                       飞鸭向前冲  nowplaying   NaN   NaN   \n",
      "18                             星愿  nowplaying   6.7   3.5   \n",
      "19                             刀尖  nowplaying   6.0   3.0   \n",
      "20                             红猪  nowplaying   8.6   4.5   \n",
      "21                         无价之宝  nowplaying   6.5   3.5   \n",
      "22                  前任4：英年早婚  nowplaying   5.7   3.0   \n",
      "23                         沉默笔录  nowplaying   6.9   3.5   \n",
      "24                             仲肯  nowplaying   NaN   NaN   \n",
      "25                   不要走散好不好  nowplaying   NaN   NaN   \n",
      "26                           诡摇铃  nowplaying   NaN   NaN   \n",
      "27                         二手杰作  nowplaying   6.7   3.5   \n",
      "28                         吾爱敦煌  nowplaying   7.1   3.5   \n",
      "29                         最后一夜  nowplaying   NaN   NaN   \n",
      "30                       靠近我一点  nowplaying   4.1   2.0   \n",
      "31                         铃芽之旅  nowplaying   7.2   4.0   \n",
      "32           迷你特工队之汉堡大作战  nowplaying   NaN   NaN   \n",
      "33  汪汪队立大功大电影2：超能大冒险  nowplaying   7.5   4.0   \n",
      "34                       最好的相遇  nowplaying   6.3   3.5   \n",
      "35                         千里送鹤  nowplaying   NaN   NaN   \n",
      "36              我是哪吒2之英雄归来  nowplaying   2.4   1.5   \n",
      "37                 贝肯熊：火星任务  nowplaying   7.2   3.5   \n",
      "38                 功夫王之萌虎上山  nowplaying   NaN   NaN   \n",
      "39                海王2：失落的王国    upcoming   NaN   NaN   \n",
      "40                         爱乐之城    upcoming   NaN   NaN   \n",
      "41                         敦煌英雄    upcoming   NaN   NaN   \n",
      "42                         人民万岁    upcoming   NaN   NaN   \n",
      "43                             潜行    upcoming   NaN   NaN   \n",
      "44                     年会不能停！    upcoming   NaN   NaN   \n",
      "45                     金色面具英雄    upcoming   NaN   NaN   \n",
      "46                           金手指    upcoming   NaN   NaN   \n",
      "47                   一闪一闪亮星星    upcoming   NaN   NaN   \n",
      "48                        非诚勿扰3    upcoming   NaN   NaN   \n",
      "\n",
      "                 时长   \n",
      "0             114分钟  \\\n",
      "1             132分钟   \n",
      "2             106分钟   \n",
      "3             110分钟   \n",
      "4             116分钟   \n",
      "5             144分钟   \n",
      "6             105分钟   \n",
      "7              99分钟   \n",
      "8             138分钟   \n",
      "9             110分钟   \n",
      "10            158分钟   \n",
      "11            131分钟   \n",
      "12            105分钟   \n",
      "13            102分钟   \n",
      "14  157分钟(中国大陆)   \n",
      "15             94分钟   \n",
      "16            121分钟   \n",
      "17             82分钟   \n",
      "18             95分钟   \n",
      "19            121分钟   \n",
      "20             94分钟   \n",
      "21            132分钟   \n",
      "22            129分钟   \n",
      "23             91分钟   \n",
      "24             87分钟   \n",
      "25             99分钟   \n",
      "26             85分钟   \n",
      "27            114分钟   \n",
      "28            101分钟   \n",
      "29             89分钟   \n",
      "30             94分钟   \n",
      "31  122分钟(中国大陆)   \n",
      "32                NaN   \n",
      "33             88分钟   \n",
      "34            110分钟   \n",
      "35             90分钟   \n",
      "36             85分钟   \n",
      "37             96分钟   \n",
      "38             82分钟   \n",
      "39            125分钟   \n",
      "40            128分钟   \n",
      "41                NaN   \n",
      "42                NaN   \n",
      "43            114分钟   \n",
      "44            117分钟   \n",
      "45                NaN   \n",
      "46            125分钟   \n",
      "47            107分钟   \n",
      "48                NaN   \n",
      "\n",
      "                                                         上映时间   \n",
      "0   2023-12-15(中国大陆)/2023-08-31(威尼斯国际电影节)/2023-09-...  \\\n",
      "1     2023-12-15(中国大陆)/2023-11-03(金鸡国产新片展)               \n",
      "2                                2023-12-16(中国大陆)               \n",
      "3               2023-12-16(中国大陆)/2023-04-14(日本)               \n",
      "4   2023-12-08(中国大陆)/2023-12-06(中国台湾)/2023-12-15(美...      \n",
      "5                                2023-11-25(中国大陆)               \n",
      "6                                2023-12-08(中国大陆)               \n",
      "7                                2023-12-08(中国大陆)               \n",
      "8                                2023-12-08(中国大陆)               \n",
      "9       2023-12-15(中国大陆)/2023-11-03(金鸡国际影展)               \n",
      "10         2023-11-17(美国/中国大陆)/2023-11-15(法国)               \n",
      "11                               2023-11-24(中国大陆)               \n",
      "12                               2023-12-01(中国大陆)               \n",
      "13  2023-10-21(中国大陆)/2023-05-20(戛纳电影节)/2023-10-11(...      \n",
      "14              2023-12-01(中国大陆)/2023-11-22(美国)               \n",
      "15              2023-11-25(中国大陆)/2023-08-04(日本)               \n",
      "16   2023-11-30(中国大陆)/2023-07-31(FIRST青年电影展)               \n",
      "17              2023-12-08(中国大陆)/2023-12-22(美国)               \n",
      "18              2023-11-24(中国大陆)/2023-11-22(美国)               \n",
      "19                               2023-11-24(中国大陆)               \n",
      "20              2023-11-17(中国大陆)/1992-07-18(日本)               \n",
      "21    2023-11-10(中国大陆)/2023-11-01(金鸡国产新片展)               \n",
      "22                               2023-09-28(中国大陆)               \n",
      "23    2023-12-01(中国大陆)/2023-10-14(平遥国际电影展)               \n",
      "24      2023-12-11(中国大陆)/2023-11-02(厦门金鸡影展)               \n",
      "25                               2023-12-01(中国大陆)               \n",
      "26                               2023-12-08(中国大陆)               \n",
      "27                               2023-10-27(中国大陆)               \n",
      "28    2023-11-17(中国大陆)/2023-06-10(上海国际电影节)               \n",
      "29    2023-12-08(中国大陆)/2022-11-04(重庆青年电影展)               \n",
      "30                               2023-11-24(中国大陆)               \n",
      "31              2023-03-24(中国大陆)/2022-11-11(日本)               \n",
      "32                               2023-11-11(中国大陆)               \n",
      "33                          2023-09-29(美国/中国大陆)               \n",
      "34   2023-10-20(中国大陆)/2023-07-24(FIRST青年电影展)               \n",
      "35   2023-11-16(中国大陆)/2022-07-30(FIRST青年电影展)               \n",
      "36                               2023-09-29(中国大陆)               \n",
      "37                               2023-09-28(中国大陆)               \n",
      "38                               2023-09-29(中国大陆)               \n",
      "39  2023-12-15(中国大陆)/2023-08-31(威尼斯国际电影节)/2023-09-...   \n",
      "40    2023-12-15(中国大陆)/2023-11-03(金鸡国产新片展)               \n",
      "41                               2023-12-16(中国大陆)               \n",
      "42              2023-12-16(中国大陆)/2023-04-14(日本)               \n",
      "43  2023-12-08(中国大陆)/2023-12-06(中国台湾)/2023-12-15(美...      \n",
      "44                               2023-11-25(中国大陆)               \n",
      "45                               2023-12-08(中国大陆)               \n",
      "46                               2023-12-08(中国大陆)               \n",
      "47                               2023-12-08(中国大陆)               \n",
      "48      2023-12-15(中国大陆)/2023-11-03(金鸡国际影展)               \n",
      "\n",
      "                                  类型           制片国家   \n",
      "0                                 剧情          法国 美国  \\\n",
      "1                            剧情/犯罪           中国大陆   \n",
      "2                  剧情/动作/悬疑/犯罪           中国大陆   \n",
      "3        动作/动画/悬疑/惊悚/犯罪/冒险               日本   \n",
      "4                  喜剧/歌舞/奇幻/冒险   美国 英国 加拿大   \n",
      "5                       剧情/悬疑/犯罪           中国大陆   \n",
      "6                       剧情/悬疑/犯罪           中国大陆   \n",
      "7                       剧情/悬疑/奇幻           中国大陆   \n",
      "8                            动作/犯罪  中国香港 中国大陆   \n",
      "9                                 剧情           中国大陆   \n",
      "10       剧情/动作/科幻/惊悚/战争/冒险               美国   \n",
      "11                           剧情/传记           中国大陆   \n",
      "12                                剧情           中国大陆   \n",
      "13                           剧情/犯罪           中国大陆   \n",
      "14       剧情/动作/传记/历史/战争/冒险          英国 美国   \n",
      "15                           喜剧/动画               日本   \n",
      "16                           剧情/犯罪           中国大陆   \n",
      "17            喜剧/动作/动画/奇幻/冒险   美国 法国 加拿大   \n",
      "18            喜剧/动画/歌舞/奇幻/冒险               美国   \n",
      "19                           剧情/悬疑           中国大陆   \n",
      "20                      喜剧/动画/冒险               日本   \n",
      "21                      剧情/喜剧/家庭           中国大陆   \n",
      "22                           喜剧/爱情           中国大陆   \n",
      "23                      剧情/悬疑/犯罪           中国大陆   \n",
      "24                                剧情           中国大陆   \n",
      "25                                爱情           中国大陆   \n",
      "26                           惊悚/恐怖           中国大陆   \n",
      "27                           剧情/喜剧           中国大陆   \n",
      "28                      剧情/传记/历史           中国大陆   \n",
      "29                           剧情/爱情           中国大陆   \n",
      "30                                爱情  中国大陆 中国香港   \n",
      "31                      爱情/动画/奇幻               日本   \n",
      "32                           动画/奇幻           中国大陆   \n",
      "33  剧情/喜剧/动作/科幻/动画/奇幻/冒险        美国 加拿大   \n",
      "34                           剧情/爱情           中国大陆   \n",
      "35                           剧情/儿童           中国大陆   \n",
      "36                 剧情/喜剧/动画/奇幻           中国大陆   \n",
      "37                 喜剧/科幻/动画/冒险           中国大陆   \n",
      "38                 喜剧/动画/奇幻/冒险           中国大陆   \n",
      "39                                剧情               美国   \n",
      "40                           剧情/犯罪               美国   \n",
      "41                 剧情/动作/悬疑/犯罪           中国大陆   \n",
      "42       动作/动画/悬疑/惊悚/犯罪/冒险           中国大陆   \n",
      "43                 喜剧/歌舞/奇幻/冒险  中国大陆 中国香港   \n",
      "44                      剧情/悬疑/犯罪           中国大陆   \n",
      "45                      剧情/悬疑/犯罪    中国大陆 加拿大   \n",
      "46                      剧情/悬疑/奇幻  中国香港 中国大陆   \n",
      "47                           动作/犯罪           中国大陆   \n",
      "48                                剧情           中国大陆   \n",
      "\n",
      "                           导演   \n",
      "0                     吕克·贝松  \\\n",
      "1                          戴墨   \n",
      "2                        马浴柯   \n",
      "3                        立川让   \n",
      "4                       保罗·金   \n",
      "5                        曹保平   \n",
      "6                          陈卓   \n",
      "7                        蔡耳朵   \n",
      "8                 林超贤 唐唯瀚   \n",
      "9                          虹影   \n",
      "10              弗朗西斯·劳伦斯   \n",
      "11                  郑大圣 杨瑾   \n",
      "12                       王小列   \n",
      "13                       魏书钧   \n",
      "14                雷德利·斯科特   \n",
      "15                       大根仁   \n",
      "16                       忻钰坤   \n",
      "17      本杰明·雷内 盖洛·霍姆西   \n",
      "18  克里斯·巴克 弗恩·维拉桑托恩   \n",
      "19                       高群书   \n",
      "20                       宫崎骏   \n",
      "21                       张大鹏   \n",
      "22                       田羽生   \n",
      "23                       郝飞环   \n",
      "24                         邢潇   \n",
      "25                  郑来志 阿猛   \n",
      "26                       毕鉴威   \n",
      "27                       王子昭   \n",
      "28                         苗月   \n",
      "29                         唐皓   \n",
      "30                       夏永康   \n",
      "31                       新海诚   \n",
      "32                          NaN   \n",
      "33                卡尔·布伦克尔   \n",
      "34                       哈智超   \n",
      "35                       拉华加   \n",
      "36                 文帝 陈朝意    \n",
      "37                        王超    \n",
      "38                          NaN   \n",
      "39                       温子仁   \n",
      "40                达米恩·查泽雷   \n",
      "41                         曹盾   \n",
      "42                         郝蕴   \n",
      "43                       关智耀   \n",
      "44                       董润年   \n",
      "45         肖恩·帕特里克·奥赖利   \n",
      "46                       庄文强   \n",
      "47                  陈小明 章攀   \n",
      "48                       冯小刚   \n",
      "\n",
      "                                                          演员   \n",
      "0   卡莱伯·兰德里·琼斯 / 克里斯托弗·邓汉 / 马里莎·贝伦森......  \\\n",
      "1                                  张译 / 李晨 / 魏晨            \n",
      "2                            张家辉 / 阮经天 / 王大陆            \n",
      "3                      高山南 / 林原惠美 / 山崎和佳奈            \n",
      "4         提莫西·查拉梅 / 奥利维娅·科尔曼 / 休·格兰特            \n",
      "5                                  黄渤 / 周迅 / 祖峰            \n",
      "6                            许光汉 / 张钧甯 / 惠英红            \n",
      "7                            章若楠 / 白宇帆 / 刘奕君            \n",
      "8                            张家辉 / 陈伟霆 / 梁洛施            \n",
      "9                                左航 / 冯家妹 / 白恩            \n",
      "10            汤姆·布莱斯 / 瑞秋·齐格勒 / 彼特·丁拉基            \n",
      "11                               海清 / 陈永胜 / 柴烨            \n",
      "12                               闫妮 / 谭松韵 / 吴京            \n",
      "13                         朱一龙 / 曾美慧孜 / 侯天来            \n",
      "14          华金·菲尼克斯 / 凡妮莎·柯比 / 塔哈·拉希姆            \n",
      "15                   小林由美子 / 楢桥美纪 / 森川智之            \n",
      "16                               周冬雨 / 宋洋 / 袁弘            \n",
      "17   库梅尔·南贾尼 / 伊丽莎白·班克斯 / 科甘-迈克尔·凯            \n",
      "18        阿丽亚娜·德博斯 / 克里斯·派恩 / 艾伦·图代克            \n",
      "19                             张译 / 黄志忠 / 郎月婷            \n",
      "20                 森山周一郎 / 加藤登纪子 / 冈村明美            \n",
      "21                             张译 / 潘斌龙 / 周依然            \n",
      "22                               韩庚 / 郑恺 / 于文文            \n",
      "23                               章宇 / 马吟吟 / 孙敏            \n",
      "24                                      李泰 / 周静波            \n",
      "25                             徐若晗 / 张开泰 / 秦奋            \n",
      "26                           邱意浓 / 丁圣霏 / 廖茉汐            \n",
      "27                           于和伟 / 郭麒麟 / 倪虹洁            \n",
      "28                             陈瑾 / 林永健 / 黄品沅            \n",
      "29                           蒋中炜 / 蔡雪晴 / 王若珊            \n",
      "30                                 张震 / 春夏 / 李现            \n",
      "31                     原菜乃华 / 松村北斗 / 深津绘里            \n",
      "32                                                NaN            \n",
      "33        麦肯娜·格瑞丝 / 塔拉吉·P·汉森 / 马尔赛·马丁            \n",
      "34                             金世佳 / 邱泽 / 张钧甯            \n",
      "35                     周拉多杰 / 朋毛样专 / 尕斗扎西            \n",
      "36                               王薇 / 藤新 / 傅晨阳            \n",
      "37                               梁晓强 / 巴赫 / 张东            \n",
      "38                                                NaN            \n",
      "39            杰森·莫玛 / 帕特里克·威尔森 / 安珀·赫德            \n",
      "40                瑞恩·高斯林 / 艾玛·斯通 / 约翰·传奇            \n",
      "41                                 章宇 / 窦骁 / 吕凉            \n",
      "42                                                NaN            \n",
      "43                           刘德华 / 林家栋 / 彭于晏            \n",
      "44                               大鹏 / 白客 / 庄达菲            \n",
      "45    朗·普尔曼 / 帕顿·奥斯瓦尔特 / 克里斯托弗·普卢默            \n",
      "46                           梁朝伟 / 刘德华 / 蔡卓妍            \n",
      "47                             屈楚萧 / 张佳宁 / 傅菁            \n",
      "48                                 葛优 / 舒淇 / 范伟            \n",
      "\n",
      "                                                                                               简介   \n",
      "0   被暴戾的父亲伤害、被囚禁在狗笼生活数年的道格（卡莱伯·兰德里·琼斯 饰），长大后只能与他的数...     \\\n",
      "1   刑侦大队队长程兵（张译 饰）带领的三大队在办理一起恶性案件的过程中导致嫌犯之一意外死亡，被判...    \n",
      "2   横跨黑白两道只手遮天的洪泰集团正值换选之际，一个神秘杀手陈安（张家辉 饰）却突然只身闯入这个...    \n",
      "3   故事发生在东京八丈岛近海，一座将全球各地警方监控相连接的高科技设施——“太平洋浮标”即将启动...       \n",
      "4   影片聚焦经典之作《查理和巧克力工厂》的主人公威利·旺卡（提莫西·查拉梅 饰），这位年轻且天赋...      \n",
      "5   老金（黄渤 饰）为供女儿金丽娜（周依然 饰）留学终日捕鱼，却换来女儿身中17刀离奇身亡的死讯...       \n",
      "6   贪婪无厌的警长郑威（许光汉 饰）得知富商之妻乔文娜（张钧甯 饰）蒙冤卷入一场密室谋杀案后主动...     \n",
      "7   深巷的尽头，一间“照明商店”长年亮着灯，神秘的老板（刘奕君 饰）细心擦拭着每一个灯泡，形形色...      \n",
      "8   缉毒总督察李振邦（张家辉 饰）和卧底警员江铭（陈伟霆 饰）秘密执行追捕毒贩计划，无奈遭遇险境...     \n",
      "9   影片根据虹影同名长篇小说改编，以重庆“山城”为地理背景，讲述了在1976年，12 岁少年窦小...            \n",
      "10  18岁的科里奥兰纳斯·斯诺（汤姆·布莱斯 饰）被选为第十届饥饿游戏的导师，与来自第十二区的贫...        \n",
      "11  这是一个坚毅的女人和一群刚刚毕业还很稚嫩的老师，带着一百多个女孩成功逆天改命的故事，是关于爱...   \n",
      "12  为了完成女儿李妍（谭松韵 饰）高三前的小小心愿，父亲李博宇（吴京 饰）决心带着女儿远赴阿勒泰...     \n",
      "13  影片改编自余华同名先锋代表作，看似是马哲（朱一龙 饰）在找寻凶案的真相，但故事没有答案，因为...    \n",
      "14  影片讲述了法国皇帝拿破仑（华金·菲尼克斯 Joaquin Phoenix 饰）从士兵到帝王波...                     \n",
      "15  故事讲述住在诺斯特拉达姆斯邻镇的努斯特拉达马斯留下了一个预言：“在 20 和 23 并列的年...            \n",
      "16                         热搜背后，真相究竟是什么？                                                 \n",
      "17  飞鸭马拉德一家向南迁徙前往热带的牙买加，却意外闯入疯狂大都市纽约？在紧张刺激的飞行之旅中，他...   \n",
      "18  在位于伊比利亚半岛外有一片梦幻之地，17岁的亚莎（阿丽亚娜·德博斯 Ariana DeBos...                   \n",
      "19  1940年，多方势力盘踞南京，特工金深水（张译 饰）终日周旋于日本军方、汪伪政府和军统之间，...        \n",
      "20  罗森是一战中意大利空军的王牌飞行员，但却被施了魔法变了一头猪。虽然变了猪，但罗森还是继续他的...   \n",
      "21  “石头”石振邦（张译 饰）和杨武（潘斌龙 饰）这对落魄兄弟经营五金店艰难维生，为追讨欠款，他...       \n",
      "22  孟云（韩庚 饰）和余飞（郑恺 饰）这对难兄难弟，在历经情场的洗礼之后，终于来到了谈婚论嫁的年...     \n",
      "23  平西镇位于西南某地，90年代乡镇治安由联防队维护，联防队长李立忠（章宇 饰）的父亲意外死亡，...      \n",
      "24  影片讲述了一个名叫仲肯的汉族男人，在藏区流浪多年，执着地想要成为格萨尔王传世故事的颂唱艺人“...    \n",
      "25  和你走到最后就是我最大的愿望。大大咧咧的夏末（徐若晗 饰）和温润内敛的初秋（张开泰 饰）见证...     \n",
      "26  影片讲述邱意浓饰演的美艳女医生误入诡宅后遇到的一系列灵异案件，晶女郎和民国恐怖元素双重 bu...      \n",
      "27  这是一个关于“儿子‘死’了，爸爸‘红’了”的荒诞故事。儿子坠楼“自杀”，父亲替儿子写了一封才...           \n",
      "28  影片记述了“敦煌女儿”，国家荣誉称号获得者樊锦诗坚守大漠，守护世界宝藏莫高窟的壮丽人生故事。...     \n",
      "29  情人节，对于陈良古（蒋中炜 饰）来说不过是无数个工作日的其中一天，避开对自己爱理不理的妻子，...    \n",
      "30  等一个人很久会有结果吗？等了初恋男友10年的林楚楚（春夏 饰），最终等来的是男友和别人的婚礼...      \n",
      "31  宁静的九州乡间小镇，生活着平凡的少女岩户铃芽（原菜乃华 配音）。这天上学路上，她邂逅了神秘的...    \n",
      "32  蓝玲市正在面临空前危机。汉堡军团在城市街头四处作乱，破坏街道和建筑，让市民们苦不堪言，但迷你...   \n",
      "33  一颗陨石从天而降，汪汪队基地惨遭破坏，因祸得福的是，汪汪队通过陨石获得了超能力！变成超能狗狗...   \n",
      "34  人生失意的B超师曹卢医（金世佳 饰）经人介绍来到了东山庭院疗养院工作。在这里，曹卢医看到了另...     \n",
      "35  影片讲述了一个青海高原藏区村落，少年多杰和姐姐格桑与奶奶一同生活。早年失去妈妈的姐弟俩渴望获...   \n",
      "36  陈塘关大战后哪吒和龙王三太子同归于尽，太乙真人将哪吒的仙体带回乾元山，等待时机将哪吒重生来帮...   \n",
      "37  最倒霉的特工贝肯熊，为了重获认可，偷偷混入火星任务，不料却闯进了火星里的神秘世界，和要抓捕的...   \n",
      "38  一夜之间，虎门武馆馆主惨遭毒手，少馆主乐乐也被神秘人物追杀，为报父仇和查清事情背后的真相，乐...   \n",
      "39  被暴戾的父亲伤害、被囚禁在狗笼生活数年的道格（卡莱伯·兰德里·琼斯 饰），长大后只能与他的数...      \n",
      "40  刑侦大队队长程兵（张译 饰）带领的三大队在办理一起恶性案件的过程中导致嫌犯之一意外死亡，被判...    \n",
      "41  横跨黑白两道只手遮天的洪泰集团正值换选之际，一个神秘杀手陈安（张家辉 饰）却突然只身闯入这个...    \n",
      "42  故事发生在东京八丈岛近海，一座将全球各地警方监控相连接的高科技设施——“太平洋浮标”即将启动...       \n",
      "43  影片聚焦经典之作《查理和巧克力工厂》的主人公威利·旺卡（提莫西·查拉梅 饰），这位年轻且天赋...      \n",
      "44  老金（黄渤 饰）为供女儿金丽娜（周依然 饰）留学终日捕鱼，却换来女儿身中17刀离奇身亡的死讯...       \n",
      "45  贪婪无厌的警长郑威（许光汉 饰）得知富商之妻乔文娜（张钧甯 饰）蒙冤卷入一场密室谋杀案后主动...     \n",
      "46  深巷的尽头，一间“照明商店”长年亮着灯，神秘的老板（刘奕君 饰）细心擦拭着每一个灯泡，形形色...      \n",
      "47  缉毒总督察李振邦（张家辉 饰）和卧底警员江铭（陈伟霆 饰）秘密执行追捕毒贩计划，无奈遭遇险境...     \n",
      "48  影片根据虹影同名长篇小说改编，以重庆“山城”为地理背景，讲述了在1976年，12 岁少年窦小...            \n",
      "\n",
      "    评价人数  \n",
      "0    21292.0  \n",
      "1    59951.0  \n",
      "2        NaN  \n",
      "3    36602.0  \n",
      "4    51017.0  \n",
      "5   268633.0  \n",
      "6    36452.0  \n",
      "7    29675.0  \n",
      "8    22957.0  \n",
      "9        NaN  \n",
      "10   52976.0  \n",
      "11       NaN  \n",
      "12   23244.0  \n",
      "13  288791.0  \n",
      "14   35688.0  \n",
      "15   22166.0  \n",
      "16   25981.0  \n",
      "17       NaN  \n",
      "18   17826.0  \n",
      "19   16917.0  \n",
      "20  250206.0  \n",
      "21   33612.0  \n",
      "22  102247.0  \n",
      "23    5150.0  \n",
      "24       NaN  \n",
      "25       NaN  \n",
      "26       NaN  \n",
      "27   36928.0  \n",
      "28    2449.0  \n",
      "29       NaN  \n",
      "30    1166.0  \n",
      "31  422227.0  \n",
      "32       NaN  \n",
      "33    5155.0  \n",
      "34    5123.0  \n",
      "35       NaN  \n",
      "36    4059.0  \n",
      "37    3610.0  \n",
      "38       NaN  \n",
      "39   21298.0  \n",
      "40   59964.0  \n",
      "41       NaN  \n",
      "42   36612.0  \n",
      "43   51019.0  \n",
      "44  268635.0  \n",
      "45   36453.0  \n",
      "46   29677.0  \n",
      "47   22958.0  \n",
      "48       NaN  \n"
     ]
    }
   ],
   "source": [
    "tmp = pd.read_csv('./data/douban.csv', sep=',', header=0, encoding='utf-8')\n",
    "print(tmp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aacf6b6e",
   "metadata": {},
   "source": [
    "### 导入html\n",
    "\n",
    "**pandas.read_html(io, match, header, encoding)**\n",
    "* io: 文件路径或URL\n",
    "* match: 正则表达式，返回与正则表达式匹配的表格，默认为None\n",
    "* header: 指定作为列名的行，默认值为0\n",
    "* encoding: 指定字符集，默认为'utf-8'\n",
    "\n",
    "`` `eg. 需要另一个名为lxml的库的帮助来解析HTML和XML文件` ``\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a18c904c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "2\n",
      "  Programming Language             Creator  Year\n",
      "0                    C      Dennis Ritchie  1972\n",
      "1               Python    Guido Van Rossum  1989\n",
      "2                 Ruby  Yukihiro Matsumoto  1995\n",
      "   Area (sq.ft)  Price (USD)\n",
      "0         12000          500\n",
      "1         32000          700\n"
     ]
    }
   ],
   "source": [
    "tmp = pd.read_html('./data/测试.html', match='.*', header=0, encoding='utf-8')\n",
    "print(type(tmp))\n",
    "print(len(tmp))\n",
    "print(tmp[0])\n",
    "print(tmp[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8885462a",
   "metadata": {},
   "source": [
    "## 数据抽取\n",
    "\n",
    "* loc: 以行名和列名作为参数，当只有一个参数时，默认是行名\n",
    "* iloc: 以行和列位置的***索引***作为参数，***0表示第一行***，当只有一个参数时，默认是行索引，抽取整行数据\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39090dff",
   "metadata": {},
   "source": [
    "### 单行数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d50ed492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A    13.0\n",
      "B    15.0\n",
      "C     NaN\n",
      "Name: d, dtype: float64\n",
      "\n",
      "A    4.0\n",
      "B    5.0\n",
      "C    6.0\n",
      "Name: c, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "tmp = pd.DataFrame(data=[[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12],\n",
    "                         [13, 15]],\n",
    "                   index=['a', 'c', 'e', 'r', 'd'],\n",
    "                   columns=['A', 'B', 'C'])\n",
    "\n",
    "print(tmp.loc['d'])\n",
    "print()\n",
    "print(tmp.iloc[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb834c25",
   "metadata": {},
   "source": [
    "### 多行数据\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b84fc00",
   "metadata": {},
   "source": [
    "#### 连续数据\n",
    "\n",
    "用冒号\n",
    "\n",
    "loc: 为闭区间 [l, r]\n",
    "\n",
    "iloc: 为开区间 [l, r)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8feff1dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B     C\n",
      "a   1   2   3.0\n",
      "c   4   5   6.0\n",
      "e   7   8   9.0\n",
      "r  10  11  12.0\n",
      "\n",
      "   A  B    C\n",
      "a  1  2  3.0\n",
      "c  4  5  6.0\n",
      "e  7  8  9.0\n"
     ]
    }
   ],
   "source": [
    "print(tmp.loc['a':'r'], '\\n')\n",
    "print(tmp.iloc[0:3])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7e7e160",
   "metadata": {},
   "source": [
    "#### 离散数据(间隔的数据)\n",
    "\n",
    "用列表\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "780e7e5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B     C\n",
      "a   1   2   3.0\n",
      "r  10  11  12.0\n",
      "\n",
      "    A   B    C\n",
      "a   1   2  3.0\n",
      "d  13  15  NaN\n"
     ]
    }
   ],
   "source": [
    "print(tmp.loc[['a', 'r']])\n",
    "print()\n",
    "print(tmp.iloc[[0, 4]])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae446d4c",
   "metadata": {},
   "source": [
    "### 单列数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "96285ee4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a     1\n",
      "c     4\n",
      "e     7\n",
      "r    10\n",
      "d    13\n",
      "Name: A, dtype: int64\n",
      "\n",
      "a     1\n",
      "c     4\n",
      "e     7\n",
      "r    10\n",
      "d    13\n",
      "Name: A, dtype: int64\n",
      "\n",
      "a     1\n",
      "c     4\n",
      "e     7\n",
      "r    10\n",
      "d    13\n",
      "Name: A, dtype: int64\n",
      "\n",
      "a     1\n",
      "c     4\n",
      "e     7\n",
      "r    10\n",
      "d    13\n",
      "Name: A, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(tmp.loc[:, 'A'])\n",
    "print()\n",
    "print(tmp.iloc[:, 0])\n",
    "print()\n",
    "print(tmp['A'])\n",
    "print()\n",
    "print(tmp.A)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0a24672",
   "metadata": {},
   "source": [
    "### 多列数据\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1bc3954",
   "metadata": {},
   "source": [
    "#### 连续数据\n",
    "\n",
    "loc: 为闭区间 [l, r]\n",
    "\n",
    "iloc: 为开区间 [l, r)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "101fa3ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B\n",
      "a   1   2\n",
      "c   4   5\n",
      "e   7   8\n",
      "r  10  11\n",
      "d  13  15\n",
      "\n",
      "    A\n",
      "a   1\n",
      "c   4\n",
      "e   7\n",
      "r  10\n",
      "d  13\n"
     ]
    }
   ],
   "source": [
    "print(tmp.loc[:, 'A':'B'])\n",
    "print()\n",
    "print(tmp.iloc[:, 0:1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9f97b98",
   "metadata": {},
   "source": [
    "#### 离散数据(间隔的数据)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7abf6c4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B\n",
      "a   1   2\n",
      "c   4   5\n",
      "e   7   8\n",
      "r  10  11\n",
      "d  13  15\n",
      "\n",
      "    A   B\n",
      "a   1   2\n",
      "c   4   5\n",
      "e   7   8\n",
      "r  10  11\n",
      "d  13  15\n",
      "\n",
      "    A     C\n",
      "a   1   3.0\n",
      "c   4   6.0\n",
      "e   7   9.0\n",
      "r  10  12.0\n",
      "d  13   NaN\n"
     ]
    }
   ],
   "source": [
    "print(tmp.loc[:, ['A', 'B']])\n",
    "print()\n",
    "print(tmp.iloc[:, [0, 1]])\n",
    "print()\n",
    "print(tmp[['A', 'C']])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b30ba2ee",
   "metadata": {},
   "source": [
    "### 指定行列(多行多列)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55c1a5dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "\n",
      "8\n",
      "\n",
      "    B     C\n",
      "e   8   9.0\n",
      "r  11  12.0\n",
      "\n",
      "    B     C\n",
      "e   8   9.0\n",
      "r  11  12.0\n"
     ]
    }
   ],
   "source": [
    "print(tmp.loc['e', 'B'])\n",
    "print()\n",
    "print(tmp.iloc[2, 1])\n",
    "print()\n",
    "print(tmp.loc[['e', 'r'], ['B', 'C']])\n",
    "print()\n",
    "print(tmp.iloc[[2, 3], [1, 2]])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4585eebe",
   "metadata": {},
   "source": [
    "### 按条件抽取数据\n",
    "\n",
    "* df[条件]\n",
    "* df.loc[条件]\n",
    "* df.query(expr, inplace=False, **kwargs)\n",
    "  + expr: 条件表达式 如果列名中有特殊符号（如空格），需要用反引号\n",
    "  + inplace: 是否原地修改\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7dbfa151",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B     C\n",
      "r  10  11  12.0\n",
      "d  13  15   NaN\n",
      "\n",
      "    A   B     C\n",
      "e   7   8   9.0\n",
      "r  10  11  12.0\n",
      "d  13  15   NaN\n",
      "\n",
      "    A   B     C\n",
      "r  10  11  12.0\n",
      "d  13  15   NaN\n"
     ]
    }
   ],
   "source": [
    "print(tmp.loc[(tmp['A'] > 7) & (tmp['B'] > 7)])\n",
    "print()\n",
    "print(tmp[(tmp['A'] > 7) | (tmp['B'] > 7)])\n",
    "print()\n",
    "print(tmp.query('`A`>7 & `B`>7'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eff380ef",
   "metadata": {},
   "source": [
    "## 增加数据\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ddd893e",
   "metadata": {},
   "source": [
    "### 增加行\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dc05db1",
   "metadata": {},
   "source": [
    "#### 一行\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c5b5fe3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B     C\n",
      "a   1   2   3.0\n",
      "c   4   5   6.0\n",
      "e   7   8   9.0\n",
      "r  10  11  12.0\n",
      "d  13  15   NaN\n",
      "f   1   2   3.0 \n",
      "\n",
      "    A   B     C\n",
      "a   1   2   3.0\n",
      "c   4   5   6.0\n",
      "e   7   8   9.0\n",
      "r  10  11  12.0\n",
      "d  13  15   NaN\n",
      "f  14  16   NaN \n",
      "\n",
      "     A    B     C\n",
      "a    1    2   3.0\n",
      "c    4    5   6.0\n",
      "e    7    8   9.0\n",
      "r   10   11  12.0\n",
      "d   13   15   NaN\n",
      "f  456  234  31.0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 直接添加\n",
    "tmp.loc['f'] = [1, 2, 3]\n",
    "print(tmp, '\\n')\n",
    "\n",
    "# 利用已有数据添加\n",
    "tmp.loc['f'] = tmp.loc['d'] + 1\n",
    "print(tmp, '\\n')\n",
    "\n",
    "# 利用字典添加\n",
    "tmp.loc['f'] = {'A': 456, 'B': 234, 'C': 31}\n",
    "print(tmp, '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78020138",
   "metadata": {},
   "source": [
    "#### 多行\n",
    "\n",
    "**pandas.concat(objs, axis, join, keys, ignore_index)**\n",
    "* objs: 参与链接的pandas对象的列表或字典。***唯一的必须参数***\n",
    "* axis: 链接的轴向，0为行链接，1为列链接。默认为0。\n",
    "* join: \"inner\", \"outer\", 指明其他轴向上的索引连接方式是交集(inner)还是并集(outer)。默认为\"outer\"。\n",
    "* keys: 用于在连接轴上形成层次化的索引，默认为None。\n",
    "    - 在连接轴上形成的索引，该连接轴上的索引用元组表示。\n",
    "* ignore_index: 忽略原数据索引，重新建立0, 1, 2, 3... 的整数索引。默认为False。\n",
    "* 其余参数详情可见 [pandas.concat用法详解](https://blog.csdn.net/Asher117/article/details/84799845)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "60ca8c61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        x                    y          \n",
      "        A       B       C    A    C    D\n",
      "g   123.0   123.0   123.0  9.0  9.0  9.0\n",
      "b   324.0   324.0   324.0  NaN  NaN  NaN\n",
      "h  4536.0  4536.0  4536.0  NaN  NaN  NaN\n",
      "i  2324.0  2324.0  2324.0  NaN  NaN  NaN\n",
      "c     NaN     NaN     NaN  8.0  8.0  8.0\n",
      "r     NaN     NaN     NaN  4.0  4.0  4.0\n",
      "z     NaN     NaN     NaN  2.0  2.0  2.0\n",
      "p     NaN     NaN     NaN  3.0  3.0  3.0 \n",
      "\n",
      "        x                    y\n",
      "        A       B       C    A\n",
      "g   123.0   123.0   123.0  9.0\n",
      "b   324.0   324.0   324.0  NaN\n",
      "h  4536.0  4536.0  4536.0  NaN\n",
      "i  2324.0  2324.0  2324.0  NaN\n",
      "c     NaN     NaN     NaN  8.0\n",
      "r     NaN     NaN     NaN  4.0\n",
      "z     NaN     NaN     NaN  2.0\n",
      "p     NaN     NaN     NaN  3.0\n"
     ]
    }
   ],
   "source": [
    "d1 = pd.DataFrame(\n",
    "    {\n",
    "        'A': [123, 324, 4536, 2324],\n",
    "        'B': [123, 324, 4536, 2324],\n",
    "        'C': [123, 324, 4536, 2324]\n",
    "    },\n",
    "    index=['g', 'b', 'h', 'i'])\n",
    "\n",
    "d2 = pd.DataFrame(\n",
    "    {\n",
    "        'A': [9, 8, 4, 2, 3],\n",
    "        'C': [9, 8, 4, 2, 3],\n",
    "        'D': [9, 8, 4, 2, 3]\n",
    "    },\n",
    "    index=['g', 'c', 'r', 'z', 'p'])\n",
    "\n",
    "# axis=0时同理\n",
    "d = pd.concat([d1, d2], axis=1, keys=['x', 'y'])\n",
    "print(d, '\\n')\n",
    "print(d.loc[:, ('x', 'A'):('y', 'A')])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4913a30",
   "metadata": {},
   "source": [
    "### 增加列\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0bd40b4",
   "metadata": {},
   "source": [
    "#### 直接添加\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f011e1d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        x                    y            x\n",
      "        A       B       C    A    C    D  E\n",
      "g   123.0   123.0   123.0  9.0  9.0  9.0  1\n",
      "b   324.0   324.0   324.0  NaN  NaN  NaN  2\n",
      "h  4536.0  4536.0  4536.0  NaN  NaN  NaN  3\n",
      "i  2324.0  2324.0  2324.0  NaN  NaN  NaN  4\n",
      "c     NaN     NaN     NaN  8.0  8.0  8.0  5\n",
      "r     NaN     NaN     NaN  4.0  4.0  4.0  6\n",
      "z     NaN     NaN     NaN  2.0  2.0  2.0  7\n",
      "p     NaN     NaN     NaN  3.0  3.0  3.0  8 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "d[('x', 'E')] = [1, 2, 3, 4, 5, 6, 7, 8]\n",
    "print(d, '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6333cafc",
   "metadata": {},
   "source": [
    "#### 指定位置添加\n",
    "\n",
    "**DataFrame.insert(location, column, value)**\n",
    "* location: 插入列的位置。位置从0开始\n",
    "* column: 插入列的列名。如果是层次化的索引(多层的)，需要用元组表示\n",
    "* value: 插入列的值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b42744d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        x                  y                 x\n",
      "        A       B       C  F    A    C    D  E\n",
      "g   123.0   123.0   123.0  0  9.0  9.0  9.0  1\n",
      "b   324.0   324.0   324.0  9  NaN  NaN  NaN  2\n",
      "h  4536.0  4536.0  4536.0  8  NaN  NaN  NaN  3\n",
      "i  2324.0  2324.0  2324.0  7  NaN  NaN  NaN  4\n",
      "c     NaN     NaN     NaN  6  8.0  8.0  8.0  5\n",
      "r     NaN     NaN     NaN  5  4.0  4.0  4.0  6\n",
      "z     NaN     NaN     NaN  4  2.0  2.0  2.0  7\n",
      "p     NaN     NaN     NaN  3  3.0  3.0  3.0  8 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "d.insert(3, ('y', 'F'), [0, 9, 8, 7, 6, 5, 4, 3])\n",
    "print(d, '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf576706",
   "metadata": {},
   "source": [
    "## 数据修改\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49fd9d3b",
   "metadata": {},
   "source": [
    "### 修改标题\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f11a4dda",
   "metadata": {},
   "source": [
    "#### 行标题\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "20876ac4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        x                  y                 x\n",
      "        A       B       C  F    A    C    D  E\n",
      "a   123.0   123.0   123.0  0  9.0  9.0  9.0  1\n",
      "b   324.0   324.0   324.0  9  NaN  NaN  NaN  2\n",
      "c  4536.0  4536.0  4536.0  8  NaN  NaN  NaN  3\n",
      "d  2324.0  2324.0  2324.0  7  NaN  NaN  NaN  4\n",
      "e     NaN     NaN     NaN  6  8.0  8.0  8.0  5\n",
      "f     NaN     NaN     NaN  5  4.0  4.0  4.0  6\n",
      "g     NaN     NaN     NaN  4  2.0  2.0  2.0  7\n",
      "h     NaN     NaN     NaN  3  3.0  3.0  3.0  8 \n",
      "\n",
      "        x                  y                 x\n",
      "        A       B       C  F    A    C    D  E\n",
      "A   123.0   123.0   123.0  0  9.0  9.0  9.0  1\n",
      "B   324.0   324.0   324.0  9  NaN  NaN  NaN  2\n",
      "C  4536.0  4536.0  4536.0  8  NaN  NaN  NaN  3\n",
      "D  2324.0  2324.0  2324.0  7  NaN  NaN  NaN  4\n",
      "E     NaN     NaN     NaN  6  8.0  8.0  8.0  5\n",
      "F     NaN     NaN     NaN  5  4.0  4.0  4.0  6\n",
      "G     NaN     NaN     NaN  4  2.0  2.0  2.0  7\n",
      "H     NaN     NaN     NaN  3  3.0  3.0  3.0  8\n"
     ]
    }
   ],
   "source": [
    "# 方法1\n",
    "d.index = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']\n",
    "print(d, '\\n')\n",
    "\n",
    "# 方法2   inplace表示 是直接修改，还是返回修改后的对象\n",
    "d.rename(mapper={\n",
    "    'a': 'A',\n",
    "    'b': 'B',\n",
    "    'c': 'C',\n",
    "    'd': 'D',\n",
    "    'e': 'E',\n",
    "    'f': 'F',\n",
    "    'g': 'G',\n",
    "    'h': 'H'\n",
    "},\n",
    "         inplace=True)\n",
    "print(d)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7216ea2",
   "metadata": {},
   "source": [
    "#### 列标题\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "91b690b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        A       B       C  D    E    F    G  H\n",
      "A   123.0   123.0   123.0  0  9.0  9.0  9.0  1\n",
      "B   324.0   324.0   324.0  9  NaN  NaN  NaN  2\n",
      "C  4536.0  4536.0  4536.0  8  NaN  NaN  NaN  3\n",
      "D  2324.0  2324.0  2324.0  7  NaN  NaN  NaN  4\n",
      "E     NaN     NaN     NaN  6  8.0  8.0  8.0  5\n",
      "F     NaN     NaN     NaN  5  4.0  4.0  4.0  6\n",
      "G     NaN     NaN     NaN  4  2.0  2.0  2.0  7\n",
      "H     NaN     NaN     NaN  3  3.0  3.0  3.0  8 \n",
      "\n",
      "        a       b       c  d    e    f    g  h\n",
      "A   123.0   123.0   123.0  0  9.0  9.0  9.0  1\n",
      "B   324.0   324.0   324.0  9  NaN  NaN  NaN  2\n",
      "C  4536.0  4536.0  4536.0  8  NaN  NaN  NaN  3\n",
      "D  2324.0  2324.0  2324.0  7  NaN  NaN  NaN  4\n",
      "E     NaN     NaN     NaN  6  8.0  8.0  8.0  5\n",
      "F     NaN     NaN     NaN  5  4.0  4.0  4.0  6\n",
      "G     NaN     NaN     NaN  4  2.0  2.0  2.0  7\n",
      "H     NaN     NaN     NaN  3  3.0  3.0  3.0  8\n"
     ]
    }
   ],
   "source": [
    "# 方法1\n",
    "d.columns = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']\n",
    "print(d, '\\n')\n",
    "\n",
    "# 方法2   inplace表示 是直接修改，还是返回修改后的对象\n",
    "d.rename(columns={\n",
    "    'A': 'a',\n",
    "    'B': 'b',\n",
    "    'C': 'c',\n",
    "    'D': 'd',\n",
    "    'E': 'e',\n",
    "    'F': 'f',\n",
    "    'G': 'g',\n",
    "    'H': 'h'\n",
    "},\n",
    "         inplace=True)\n",
    "print(d)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f38a4a57",
   "metadata": {},
   "source": [
    "### 修改数据\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e06a33b7",
   "metadata": {},
   "source": [
    "#### 单值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "66b36a4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        a       b       c  d    e    f    g  h\n",
      "A   123.0   123.0   123.0  0  9.0  9.0  9.0  1\n",
      "B   324.0   324.0   324.0  9  NaN  NaN  NaN  2\n",
      "C  4536.0  4536.0  4536.0  8  NaN  NaN  NaN  3\n",
      "D  2324.0  2324.0  2324.0  7  NaN  NaN  NaN  4\n",
      "E     NaN    95.0     NaN  6  8.0  8.0  8.0  5\n",
      "F     NaN     NaN     NaN  5  4.0  4.0  4.0  6\n",
      "G     NaN     NaN     NaN  4  2.0  2.0  2.0  7\n",
      "H     NaN     NaN     NaN  3  3.0  3.0  3.0  8 \n",
      "\n",
      "        a       b       c  d    e    f    g  h\n",
      "A   123.0   123.0   123.0  0  9.0  9.0  9.0  1\n",
      "B   324.0   324.0   324.0  9  NaN  NaN  NaN  2\n",
      "C  4536.0  4536.0  4536.0  8  NaN  NaN  NaN  3\n",
      "D  2324.0  2324.0  2324.0  7  NaN  NaN  NaN  4\n",
      "E     NaN    95.0     NaN  6  8.0  8.0  8.0  5\n",
      "F     NaN    96.0     NaN  5  4.0  4.0  4.0  6\n",
      "G     NaN     NaN     NaN  4  2.0  2.0  2.0  7\n",
      "H     NaN     NaN     NaN  3  3.0  3.0  3.0  8 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "d.loc['E', 'b'] = 95\n",
    "print(d, '\\n')\n",
    "d.iloc[5, 1] = 96\n",
    "print(d, '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c7fb5d",
   "metadata": {},
   "source": [
    "#### 整行(列)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3330f5db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        a       b       c  d    e    f    g    h\n",
      "A   123.0   123.0   123.0  0  9.0  9.0  9.0  1.0\n",
      "B   324.0   324.0   324.0  9  NaN  NaN  NaN  2.0\n",
      "C  4536.0  4536.0  4536.0  8  NaN  NaN  NaN  3.0\n",
      "D  2324.0  2324.0  2324.0  7  NaN  NaN  NaN  4.0\n",
      "E     NaN    95.0     NaN  6  8.0  8.0  8.0  5.0\n",
      "F     NaN    96.0     NaN  5  4.0  4.0  4.0  6.0\n",
      "G     1.0     2.0     3.0  4  5.0  6.0  7.0  NaN\n",
      "H     NaN     NaN     NaN  3  3.0  3.0  3.0  8.0 \n",
      "\n",
      "        a       b       c    d    e    f    g    h\n",
      "A   123.0   123.0   123.0  0.0  9.0  9.0  9.0  1.0\n",
      "B   324.0   324.0   324.0  9.0  NaN  NaN  NaN  2.0\n",
      "C  4536.0  4536.0  4536.0  8.0  NaN  NaN  NaN  3.0\n",
      "D  2324.0  2324.0  2324.0  7.0  NaN  NaN  NaN  4.0\n",
      "E     NaN    95.0     NaN  6.0  8.0  8.0  8.0  5.0\n",
      "F     NaN    96.0     NaN  5.0  4.0  4.0  4.0  6.0\n",
      "G     1.0     2.0     3.0  4.0  5.0  6.0  7.0  NaN\n",
      "H     NaN     NaN     NaN  NaN  NaN  NaN  NaN  NaN \n",
      "\n",
      "      a       b       c    d    e    f    g    h\n",
      "A  <NA>   123.0   123.0  0.0  9.0  9.0  9.0  1.0\n",
      "B  <NA>   324.0   324.0  9.0  NaN  NaN  NaN  2.0\n",
      "C  <NA>  4536.0  4536.0  8.0  NaN  NaN  NaN  3.0\n",
      "D  <NA>  2324.0  2324.0  7.0  NaN  NaN  NaN  4.0\n",
      "E  <NA>    95.0     NaN  6.0  8.0  8.0  8.0  5.0\n",
      "F  <NA>    96.0     NaN  5.0  4.0  4.0  4.0  6.0\n",
      "G  <NA>     2.0     3.0  4.0  5.0  6.0  7.0  NaN\n",
      "H  <NA>     NaN     NaN  NaN  NaN  NaN  NaN  NaN \n",
      "\n",
      "      a       b       c    d    e    f    g     h\n",
      "A  <NA>   123.0   123.0  0.0  9.0  9.0  9.0  <NA>\n",
      "B  <NA>   324.0   324.0  9.0  NaN  NaN  NaN  <NA>\n",
      "C  <NA>  4536.0  4536.0  8.0  NaN  NaN  NaN  <NA>\n",
      "D  <NA>  2324.0  2324.0  7.0  NaN  NaN  NaN  <NA>\n",
      "E  <NA>    95.0     NaN  6.0  8.0  8.0  8.0  <NA>\n",
      "F  <NA>    96.0     NaN  5.0  4.0  4.0  4.0  <NA>\n",
      "G  <NA>     2.0     3.0  4.0  5.0  6.0  7.0  <NA>\n",
      "H  <NA>     NaN     NaN  NaN  NaN  NaN  NaN  <NA> \n",
      "\n"
     ]
    }
   ],
   "source": [
    "d.loc['G'] = [1, 2, 3, 4, 5, 6, 7, pd.NA]  #整行修改\n",
    "print(d, '\\n')\n",
    "\n",
    "d.iloc[-1, :] = [pd.NA for _ in range(8)]  #整行修改\n",
    "print(d, '\\n')\n",
    "\n",
    "d.loc[:, 'a'] = [pd.NA for _ in range(8)]  #整列修改\n",
    "print(d, '\\n')\n",
    "\n",
    "d.iloc[:, -1] = [pd.NA for _ in range(8)]  #整列修改\n",
    "print(d, '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e3f21b5",
   "metadata": {},
   "source": [
    "## 数据删除\n",
    "\n",
    "**DataFrame.drop(label, axis, index, columns, level, inplace, errors)**\n",
    "* label：表示行或列标签。\n",
    "* axis：0表示按行删除，1表示按列删除，默认0。\n",
    "* index：删除行。\n",
    "* columns：删除列。\n",
    "* level：针对有两级索引的数据。\n",
    "* inplace：对原数组作出修改并返回一个新数组，默认False，True表示直接替换原数组。\n",
    "* errors：默认raise，ignore取消错误。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c7f57a3",
   "metadata": {},
   "source": [
    "### 删除指定行/列\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e05c8c29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        b       c    d    e    f    g     h\n",
      "A   123.0   123.0  0.0  9.0  9.0  9.0  <NA>\n",
      "B   324.0   324.0  9.0  NaN  NaN  NaN  <NA>\n",
      "C  4536.0  4536.0  8.0  NaN  NaN  NaN  <NA>\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN  <NA>\n",
      "E    95.0     NaN  6.0  8.0  8.0  8.0  <NA>\n",
      "F    96.0     NaN  5.0  4.0  4.0  4.0  <NA>\n",
      "G     2.0     3.0  4.0  5.0  6.0  7.0  <NA>\n",
      "H     NaN     NaN  NaN  NaN  NaN  NaN  <NA> \n",
      "\n",
      "        b       c    d    e    f    g\n",
      "A   123.0   123.0  0.0  9.0  9.0  9.0\n",
      "B   324.0   324.0  9.0  NaN  NaN  NaN\n",
      "C  4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "E    95.0     NaN  6.0  8.0  8.0  8.0\n",
      "F    96.0     NaN  5.0  4.0  4.0  4.0\n",
      "G     2.0     3.0  4.0  5.0  6.0  7.0\n",
      "H     NaN     NaN  NaN  NaN  NaN  NaN \n",
      "\n",
      "        b       c    d    e    f    g\n",
      "B   324.0   324.0  9.0  NaN  NaN  NaN\n",
      "C  4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "E    95.0     NaN  6.0  8.0  8.0  8.0\n",
      "F    96.0     NaN  5.0  4.0  4.0  4.0\n",
      "G     2.0     3.0  4.0  5.0  6.0  7.0\n",
      "H     NaN     NaN  NaN  NaN  NaN  NaN \n",
      "\n",
      "        b       c    d    e    f    g\n",
      "C  4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "E    95.0     NaN  6.0  8.0  8.0  8.0\n",
      "F    96.0     NaN  5.0  4.0  4.0  4.0\n",
      "G     2.0     3.0  4.0  5.0  6.0  7.0\n",
      "H     NaN     NaN  NaN  NaN  NaN  NaN \n",
      "\n"
     ]
    }
   ],
   "source": [
    "d.drop(['a'], axis=1, inplace=True)  # 删除'a'列\n",
    "print(d, '\\n')\n",
    "\n",
    "d.drop(labels='h', axis=1, inplace=True)  # 删除'h'列\n",
    "print(d, '\\n')\n",
    "\n",
    "d.drop(['A'], inplace=True)  # 删除'A'行\n",
    "print(d, '\\n')\n",
    "\n",
    "d.drop(labels='B', axis=0, inplace=True)  # 删除'B'行\n",
    "print(d, '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41841ea9",
   "metadata": {},
   "source": [
    "### 删除特定条件数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6d579c27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        b       c    d    e    f    g\n",
      "C  4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "E    95.0     NaN  6.0  8.0  8.0  8.0\n",
      "F    96.0     NaN  5.0  4.0  4.0  4.0\n",
      "H     NaN     NaN  NaN  NaN  NaN  NaN \n",
      "\n",
      "        b       c    d    e    f    g\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "F    96.0     NaN  5.0  4.0  4.0  4.0\n",
      "G     2.0     3.0  4.0  5.0  6.0  7.0\n",
      "H     NaN     NaN  NaN  NaN  NaN  NaN \n",
      "\n",
      "        b       c    d    e    f    g\n",
      "C  4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "G     2.0     3.0  4.0  5.0  6.0  7.0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "tmp = d.drop(d[d['b'] < 10].index)  # 删除'b'的值小于10行\n",
    "print(tmp, '\\n')\n",
    "\n",
    "tmp = d.drop(d[d['d'].isin([8, 6])].index)  # 删除'd'的值是8或6的行\n",
    "print(tmp, '\\n')\n",
    "\n",
    "tmp = d.drop(d[d['c'].isna()].index)  # 删除'c'的值为空的行\n",
    "print(tmp, '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0e32a52",
   "metadata": {},
   "source": [
    "## 数据清洗\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e4536d",
   "metadata": {},
   "source": [
    "### 缺失值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b0dc193d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       b      c      d      e      f      g\n",
      "C  False  False  False   True   True   True\n",
      "D  False  False  False   True   True   True\n",
      "E  False   True  False  False  False  False\n",
      "F  False   True  False  False  False  False\n",
      "G  False  False  False  False  False  False\n",
      "H   True   True   True   True   True   True \n",
      "\n",
      "       b      c      d      e      f      g\n",
      "C   True   True   True  False  False  False\n",
      "D   True   True   True  False  False  False\n",
      "E   True  False   True   True   True   True\n",
      "F   True  False   True   True   True   True\n",
      "G   True   True   True   True   True   True\n",
      "H  False  False  False  False  False  False \n",
      "\n",
      "       b      c      d      e      f      g\n",
      "C  False  False  False   True   True   True\n",
      "D  False  False  False   True   True   True\n",
      "E  False   True  False  False  False  False\n",
      "F  False   True  False  False  False  False\n",
      "G  False  False  False  False  False  False\n",
      "H   True   True   True   True   True   True\n"
     ]
    }
   ],
   "source": [
    "print(d.isnull(), '\\n')\n",
    "print(d.notnull(), '\\n')\n",
    "print(d.isna())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b1bcae8",
   "metadata": {},
   "source": [
    "#### 删除\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7b8ce4d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   电影名        状态  评分  星级\n",
      "0                    狗神  nowplaying   8.3   4.5\n",
      "1                  三大队  nowplaying   7.9   4.0\n",
      "3  名侦探柯南：黑铁的鱼影  nowplaying   6.7   3.5\n",
      "4                    旺卡  nowplaying   7.6   4.0\n",
      "5            涉过愤怒的海  nowplaying   7.5   4.0\n",
      "6                瞒天过海  nowplaying   6.3   3.5\n",
      "7                照明商店  nowplaying   6.5   3.5\n",
      "8                  爆裂点  nowplaying   6.5   3.5 \n",
      "\n",
      "                             电影名        状态  评分  星级\n",
      "0                              狗神  nowplaying   8.3   4.5\n",
      "1                            三大队  nowplaying   7.9   4.0\n",
      "2                              怒潮  nowplaying   NaN   NaN\n",
      "3            名侦探柯南：黑铁的鱼影  nowplaying   6.7   3.5\n",
      "4                              旺卡  nowplaying   7.6   4.0\n",
      "5                      涉过愤怒的海  nowplaying   7.5   4.0\n",
      "6                          瞒天过海  nowplaying   6.3   3.5\n",
      "7                          照明商店  nowplaying   6.5   3.5\n",
      "8                            爆裂点  nowplaying   6.5   3.5\n",
      "9                          月光武士  nowplaying   NaN   NaN\n",
      "10           饥饿游戏：鸣鸟与蛇之歌  nowplaying   7.0   3.5\n",
      "11                       我本是高山  nowplaying   NaN   NaN\n",
      "12                     再见，李可乐  nowplaying   6.7   3.5\n",
      "13                       河边的错误  nowplaying   7.3   4.0\n",
      "14                           拿破仑  nowplaying   6.5   3.5\n",
      "15   蜡笔小新：新次元！超能力大决战  nowplaying   8.1   4.0\n",
      "16                             热搜  nowplaying   6.3   3.5\n",
      "17                       飞鸭向前冲  nowplaying   NaN   NaN\n",
      "18                             星愿  nowplaying   6.7   3.5\n",
      "19                             刀尖  nowplaying   6.0   3.0\n",
      "20                             红猪  nowplaying   8.6   4.5\n",
      "21                         无价之宝  nowplaying   6.5   3.5\n",
      "22                  前任4：英年早婚  nowplaying   5.7   3.0\n",
      "23                         沉默笔录  nowplaying   6.9   3.5\n",
      "24                             仲肯  nowplaying   NaN   NaN\n",
      "25                   不要走散好不好  nowplaying   NaN   NaN\n",
      "26                           诡摇铃  nowplaying   NaN   NaN\n",
      "27                         二手杰作  nowplaying   6.7   3.5\n",
      "28                         吾爱敦煌  nowplaying   7.1   3.5\n",
      "29                         最后一夜  nowplaying   NaN   NaN\n",
      "30                       靠近我一点  nowplaying   4.1   2.0\n",
      "31                         铃芽之旅  nowplaying   7.2   4.0\n",
      "32           迷你特工队之汉堡大作战  nowplaying   NaN   NaN\n",
      "33  汪汪队立大功大电影2：超能大冒险  nowplaying   7.5   4.0\n",
      "34                       最好的相遇  nowplaying   6.3   3.5\n",
      "35                         千里送鹤  nowplaying   NaN   NaN\n",
      "36              我是哪吒2之英雄归来  nowplaying   2.4   1.5\n",
      "37                 贝肯熊：火星任务  nowplaying   7.2   3.5\n",
      "38                 功夫王之萌虎上山  nowplaying   NaN   NaN \n",
      "\n",
      "        b       c    d    e    f    g\n",
      "C  4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "E    95.0     NaN  6.0  8.0  8.0  8.0\n",
      "F    96.0     NaN  5.0  4.0  4.0  4.0\n",
      "G     2.0     3.0  4.0  5.0  6.0  7.0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 删除缺失值\n",
    "tmp = df.iloc[0:10, 0:4].dropna(inplace=False)\n",
    "print(tmp, '\\n')\n",
    "\n",
    "tmp = df[df['状态'] == 'nowplaying'].iloc[:, 0:4]\n",
    "print(tmp, '\\n')\n",
    "\n",
    "# 选择b列中不为空的行\n",
    "tmp = d[d['b'].notna()]\n",
    "print(tmp, '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6459c1f",
   "metadata": {},
   "source": [
    "#### 填充\n",
    "\n",
    "对于缺失值比例高于某个值，可以选择放弃该指标，进行删除处理\n",
    "**DataFrame.fillna(value, method, axis, inplace, limit, downcast)**\n",
    "* value：用于填充的空值的值。\n",
    "* method： {'backfill', 'bfill', 'pad', 'ffill', None}, default None。\n",
    "    - pad / ffill表示用前面行/列的值，填充当前行/列的空值\n",
    "    - backfill / bfill表示用后面行/列的值，填充当前行/列的空值。\n",
    "* axis：轴。0或'index'，表示按行删除；1或'columns'，表示按列删除。\n",
    "* inplace：是否原地替换。布尔值，默认为False。如果为True，则在原DataFrame上进行操作，返回值为None。\n",
    "* limit：int， default None。如果method被指定，对于连续的空值，这段连续区域，最多填充前 limit 个空值（如果存在多段连续区域，每段最多填充前 limit 个空值）。如果method未被指定， 在该axis下，最多填充前 limit 个空值（不论空值连续区间是否间断）\n",
    "* downcast：dict, default is None，字典中的项为，为类型向下转换规则。或者为字符串“infer”，此时会在合适的等价类型之间进行向下转换，比如float64 to int64 if possible。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "883d5179",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             电影名        状态  评分\n",
      "0                              狗神  nowplaying   8.3\n",
      "1                            三大队  nowplaying   7.9\n",
      "2                              怒潮  nowplaying   NaN\n",
      "3            名侦探柯南：黑铁的鱼影  nowplaying   6.7\n",
      "4                              旺卡  nowplaying   7.6\n",
      "5                      涉过愤怒的海  nowplaying   7.5\n",
      "6                          瞒天过海  nowplaying   6.3\n",
      "7                          照明商店  nowplaying   6.5\n",
      "8                            爆裂点  nowplaying   6.5\n",
      "9                          月光武士  nowplaying   NaN\n",
      "10           饥饿游戏：鸣鸟与蛇之歌  nowplaying   7.0\n",
      "11                       我本是高山  nowplaying   NaN\n",
      "12                     再见，李可乐  nowplaying   6.7\n",
      "13                       河边的错误  nowplaying   7.3\n",
      "14                           拿破仑  nowplaying   6.5\n",
      "15   蜡笔小新：新次元！超能力大决战  nowplaying   8.1\n",
      "16                             热搜  nowplaying   6.3\n",
      "17                       飞鸭向前冲  nowplaying   NaN\n",
      "18                             星愿  nowplaying   6.7\n",
      "19                             刀尖  nowplaying   6.0\n",
      "20                             红猪  nowplaying   8.6\n",
      "21                         无价之宝  nowplaying   6.5\n",
      "22                  前任4：英年早婚  nowplaying   5.7\n",
      "23                         沉默笔录  nowplaying   6.9\n",
      "24                             仲肯  nowplaying   NaN\n",
      "25                   不要走散好不好  nowplaying   NaN\n",
      "26                           诡摇铃  nowplaying   NaN\n",
      "27                         二手杰作  nowplaying   6.7\n",
      "28                         吾爱敦煌  nowplaying   7.1\n",
      "29                         最后一夜  nowplaying   NaN\n",
      "30                       靠近我一点  nowplaying   4.1\n",
      "31                         铃芽之旅  nowplaying   7.2\n",
      "32           迷你特工队之汉堡大作战  nowplaying   NaN\n",
      "33  汪汪队立大功大电影2：超能大冒险  nowplaying   7.5\n",
      "34                       最好的相遇  nowplaying   6.3\n",
      "35                         千里送鹤  nowplaying   NaN\n",
      "36              我是哪吒2之英雄归来  nowplaying   2.4\n",
      "37                 贝肯熊：火星任务  nowplaying   7.2\n",
      "38                 功夫王之萌虎上山  nowplaying   NaN\n",
      "39                海王2：失落的王国    upcoming   0.0\n",
      "40                         爱乐之城    upcoming   0.0\n",
      "41                         敦煌英雄    upcoming   0.0\n",
      "42                         人民万岁    upcoming   0.0\n",
      "43                             潜行    upcoming   0.0\n",
      "44                     年会不能停！    upcoming   0.0\n",
      "45                     金色面具英雄    upcoming   0.0\n",
      "46                           金手指    upcoming   0.0\n",
      "47                   一闪一闪亮星星    upcoming   0.0\n",
      "48                        非诚勿扰3    upcoming   0.0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 将状态为'upcoming'的行的评分中的空值设为0\n",
    "tmp = df.copy()\n",
    "tmp[tmp['状态'] == 'upcoming'] = tmp[tmp['状态'] == 'upcoming'].fillna(0)\n",
    "print(tmp.iloc[:, 0:3], '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83e9cbd6",
   "metadata": {},
   "source": [
    "#### 线性插值\n",
    "\n",
    "**DataFrame.interpolate(method=‘linear', axis=0, limit=None, inplace=False, limit_direction=None, limit_area=None, downcast=None, \\*\\*kwargs)**\n",
    "* method ： str，默认为 linear 使用插值方法。可用的插值方法：\n",
    "    - linear：忽略索引，线性等距插值。这是MultiIndexes支持的唯一方法。\n",
    "    - time: 在以天或者更高频率的数据上插入给定的时间间隔长度数据。\n",
    "    - index, values: 使用索引的实际数值。\n",
    "    - pad：使用现有值填写NaN。\n",
    "    - nearest, zero, slinear, quadratic, cubic, spline, barycentric, polynomial: 传递给 scipy.interpolate.interp1d。这些方法使用索引的数值。polynomial 和 spline 都要求您还指定一个顺序（int），例如 ，df.interpolate(method='polynomial', order=5)\n",
    "    - krogh, piecewise_polynomial, spline, pchip, akima：包括类似名称的SciPy插值方法。\n",
    "    - from_derivatives：指 scipy.interpolate.BPoly.from_derivatives，它替换了scipy 0.18中的piecewise_polynomial插值方法。\n",
    "* axis ： {0或'index', 1或'columns', None}, 默认为None；沿轴进行interpolate。\n",
    "* limit ： int, 要填充的连续NaN的最大数量。必须大于0。\n",
    "* inplace ： bool, 默认为False；如果可以，更新现有数据。\n",
    "* limit_direction ： {'forward'，'backward'，'both'}，默认为 forward；如果指定了限制，则将沿该方向填充连续的NaN。\n",
    "* limit_area ： {None, 'inside', 'outside'}, 默认为None；如果指定了限制，则连续的NaN将填充此限制。\n",
    "    - None：无填充限制。\n",
    "    - inside：仅填充有效值包围的NaN。\n",
    "    - outside: 仅在有效值之外填充NaN。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c4481bad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   电影名        状态  评分  星级\n",
      "0                    狗神  nowplaying   8.3   4.5\n",
      "1                  三大队  nowplaying   7.9   4.0\n",
      "2                    怒潮  nowplaying   NaN   NaN\n",
      "3  名侦探柯南：黑铁的鱼影  nowplaying   6.7   3.5\n"
     ]
    },
    {
     "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>电影名</th>\n",
       "      <th>状态</th>\n",
       "      <th>评分</th>\n",
       "      <th>星级</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>狗神</td>\n",
       "      <td>nowplaying</td>\n",
       "      <td>8.3</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>三大队</td>\n",
       "      <td>nowplaying</td>\n",
       "      <td>7.9</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>怒潮</td>\n",
       "      <td>nowplaying</td>\n",
       "      <td>7.3</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>名侦探柯南：黑铁的鱼影</td>\n",
       "      <td>nowplaying</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   电影名        状态  评分  星级\n",
       "0                    狗神  nowplaying   8.3  4.50\n",
       "1                  三大队  nowplaying   7.9  4.00\n",
       "2                    怒潮  nowplaying   7.3  3.75\n",
       "3  名侦探柯南：黑铁的鱼影  nowplaying   6.7  3.50"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = df.loc[0:3, '电影名':'星级']\n",
    "print(tmp)\n",
    "tmp.interpolate(method='linear', axis=0, inplace=True)\n",
    "tmp\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "9758710d",
   "metadata": {},
   "source": [
    "#### 双线性插值\n",
    "\n",
    "已知函数 f 在 Q11 = (x1, y1)、Q12 = (x1, y2), Q21 = (x2, y1) 以及 Q22 = (x2, y2) 四个点的值。求未知函数 f 在点 P = (x, y) 的值。\n",
    "$$\n",
    "f(x, y) = \\frac{f(Q_{11})}{(x_2-x_1)(y_2-y_1)}(x_2-x)(y_2-y) +\\frac{f(Q_{21})}{(x_2-x_1)(y_2-y_1)}(x-x_1)(y_2-y)+\\frac{f(Q_{12})}{(x_2-x_1)(y_2-y_1)}(x_2-x)(y-y_1)\n",
    "$$\n",
    "\n",
    "![image.png](attachment:image.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c48a05",
   "metadata": {},
   "source": [
    "### 重复值\n",
    "\n",
    "对于数据中存在的重复数据，包括重复的行或者某几行中的某几列的值重复（视具体数据来定），一般做删除处理，主要使用drop_duplicates方法。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc178be3",
   "metadata": {},
   "source": [
    "#### 查看\n",
    "\n",
    "**DataFrame.duplicated(subset=None, keep='first')**\n",
    "* subset: 查找特定列上的重复项，默认为None\n",
    "* keep: \n",
    "    - first: 默认值，对于每一组重复的值，第一次出现的值设置为False，其他所有值设置为True \n",
    "    - last: 每组重复值的最后一次出现被设置为False，而其他所有重复值被设置为True。\n",
    "    - False: 所有重复项都为True\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "026cf8a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        b       c    d    e    f    g\n",
      "C  4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "E    95.0     NaN  6.0  8.0  8.0  8.0\n",
      "F    96.0     NaN  5.0  4.0  4.0  4.0\n",
      "G     2.0     3.0  4.0  5.0  6.0  7.0\n",
      "H     NaN     NaN  NaN  NaN  NaN  NaN\n",
      "I    95.0     NaN  6.0  8.0  8.0  8.0 \n",
      "\n",
      "C    False\n",
      "D    False\n",
      "E    False\n",
      "F    False\n",
      "G    False\n",
      "H    False\n",
      "I     True\n",
      "dtype: bool \n",
      "\n",
      "C    False\n",
      "D    False\n",
      "E    False\n",
      "F     True\n",
      "G    False\n",
      "H     True\n",
      "I     True\n",
      "dtype: bool \n",
      "\n",
      "C    False\n",
      "D    False\n",
      "E     True\n",
      "F     True\n",
      "G    False\n",
      "H     True\n",
      "I    False\n",
      "dtype: bool \n",
      "\n",
      "C    False\n",
      "D    False\n",
      "E     True\n",
      "F     True\n",
      "G    False\n",
      "H     True\n",
      "I     True\n",
      "dtype: bool \n",
      "\n"
     ]
    }
   ],
   "source": [
    "d.loc['I'] = d.loc['E']\n",
    "print(d, '\\n')\n",
    "print(d.duplicated(), '\\n')\n",
    "print(d.duplicated(['c']), '\\n')\n",
    "print(d.duplicated(['c'], keep='last'), '\\n')\n",
    "print(d.duplicated(['c'], keep=False), '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "532ebdaf",
   "metadata": {},
   "source": [
    "#### 删除\n",
    "\n",
    "**DataFrame.drop_duplicates(subset=None, keep='first', inplace=False)**\n",
    "* subset: 查找特定列上的重复项，默认为None\n",
    "* keep: \n",
    "    - first: 默认值，对于每一组重复的值，保留第一次出现的值\n",
    "    - last: 每组重复值的最后一次出现被保留，而其他所有重复值被删除\n",
    "    - False: 所有重复项都删除\n",
    "* inplace: 是否原地修改，默认为False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "020cb159",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        b       c    d    e    f    g\n",
      "C  4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "E    95.0     NaN  6.0  8.0  8.0  8.0\n",
      "F    96.0     NaN  5.0  4.0  4.0  4.0\n",
      "G     2.0     3.0  4.0  5.0  6.0  7.0\n",
      "H     NaN     NaN  NaN  NaN  NaN  NaN\n",
      "I    95.0     NaN  6.0  8.0  8.0  8.0 \n",
      "\n",
      "        b       c    d    e    f    g\n",
      "C  4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "E    95.0     NaN  6.0  8.0  8.0  8.0\n",
      "F    96.0     NaN  5.0  4.0  4.0  4.0\n",
      "G     2.0     3.0  4.0  5.0  6.0  7.0\n",
      "H     NaN     NaN  NaN  NaN  NaN  NaN \n",
      "\n",
      "        b       c    d    e    f    g\n",
      "C  4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "F    96.0     NaN  5.0  4.0  4.0  4.0\n",
      "G     2.0     3.0  4.0  5.0  6.0  7.0\n",
      "H     NaN     NaN  NaN  NaN  NaN  NaN\n",
      "I    95.0     NaN  6.0  8.0  8.0  8.0 \n",
      "\n",
      "        b       c    d    e    f    g\n",
      "C  4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "F    96.0     NaN  5.0  4.0  4.0  4.0\n",
      "G     2.0     3.0  4.0  5.0  6.0  7.0\n",
      "H     NaN     NaN  NaN  NaN  NaN  NaN \n",
      "\n",
      "        b       c    d    e    f    g\n",
      "C  4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "G     2.0     3.0  4.0  5.0  6.0  7.0\n",
      "I    95.0     NaN  6.0  8.0  8.0  8.0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(d, '\\n')\n",
    "tmp = d.drop_duplicates()\n",
    "print(tmp, '\\n')\n",
    "tmp = d.drop_duplicates(keep='last')\n",
    "print(tmp, '\\n')\n",
    "tmp = d.drop_duplicates(keep=False)\n",
    "print(tmp, '\\n')\n",
    "tmp = d.drop_duplicates(subset=['c'], keep='last')\n",
    "print(tmp, '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bb31036",
   "metadata": {},
   "source": [
    "### 异常值\n",
    "\n",
    "异常值指超出或低于正常范围的值\n",
    "\n",
    "判断：\n",
    "* 根据给定的数据范围判断\n",
    "* 均方差：如果一个数据分布近似正态分布，那么大约68%的数据值会在均值的一个标准差范围内，大约95%的数据值会在均值的两个标准差范围内，大约99.7%的数据值会在均值的三个标准差范围内。\n",
    "* 箱形图：显示一组数据分散情况的统计图。可以将数据通过四分位数进行图形化描述，通过数据的上限和下限作为数据分布的边界。\n",
    "\n",
    "处理：\n",
    "* 删除（最常用）\n",
    "* 将异常值作为缺失值处理\n",
    "* 将异常值作为特殊情况进行分析，研究异常出现的原因\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3fc420a",
   "metadata": {},
   "source": [
    "### 索引设置\n",
    "\n",
    "便于查询数据\n",
    "\n",
    "* 如果索引是唯一的，Pandas会实验哈希表优化，查找数据的时间复杂度为O(1)。\n",
    "* 如果索引不是唯一的，但是有序，Pandas会使用二分查找算法，查找数据的时间复杂度为O(logN)。\n",
    "* 如果索引是完全随机的，那么每次查询都要扫描数据表，查找数据的时间复杂度为O(N)。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce68f602",
   "metadata": {},
   "source": [
    "#### 设置索引\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe87e448",
   "metadata": {},
   "source": [
    "##### reindex\n",
    "\n",
    "**DataFrame.reindex(labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)**\n",
    "* labels: 标签。\n",
    "* index: 行索引。\n",
    "* columns: 列索引。\n",
    "* axis:0表示行，1表示列。\n",
    "* method: 重新设置索引时，选择插值方法。\n",
    "  + bfill/backfill(向后填充)。\n",
    "  + fill/pad(向前填充)。\n",
    "* fill_value: 缺失值要填充的数据，如fill_value=0，用0填充。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2ebfdb8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        b       c    d    e    f    g\n",
      "C  4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "G     2.0     3.0  4.0  5.0  6.0  7.0\n",
      "I    95.0     NaN  6.0  8.0  8.0  8.0 \n",
      "\n",
      "        b       c    d   e   f   g\n",
      "A     NaN     NaN  NaN NaN NaN NaN\n",
      "B     NaN     NaN  NaN NaN NaN NaN\n",
      "C  4536.0  4536.0  8.0 NaN NaN NaN\n",
      "D  2324.0  2324.0  7.0 NaN NaN NaN\n",
      "e     NaN     NaN  NaN NaN NaN NaN \n",
      "\n",
      "        b       c    d    e    f    g\n",
      "A     NaN     NaN  NaN  NaN  NaN  NaN\n",
      "B     NaN     NaN  NaN  NaN  NaN  NaN\n",
      "C  4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "e     NaN     NaN  NaN  NaN  NaN  NaN\n",
      "I    95.0     NaN  6.0  8.0  8.0  8.0\n",
      "G     2.0     3.0  4.0  5.0  6.0  7.0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(tmp, '\\n')\n",
    "print(tmp.reindex(index=['A', 'B', 'C', 'D', 'e']), '\\n')\n",
    "print(tmp.reindex(index=['A', 'B', 'C', 'D', 'e', 'I', 'G']), '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5ab021c",
   "metadata": {},
   "source": [
    "##### set_index\n",
    "\n",
    "设置索引\n",
    "\n",
    "**DataFrame.set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False)**\n",
    "* keys : 要设置为索引的列名（如有多个应放在一个列表里）\n",
    "* drop : 将设置为索引的列删除，默认为True\n",
    "* append : 是否将新的索引追加到原索引后（即是否保留原索引），默认为False\n",
    "* inplace : 是否在原DataFrame上修改，默认为False\n",
    "* verify_integrity : 是否检查索引有无重复，默认为False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "43105586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        b       c    d    e    f    g\n",
      "C  4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "D  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "G     2.0     3.0  4.0  5.0  6.0  7.0\n",
      "I    95.0     NaN  6.0  8.0  8.0  8.0 \n",
      "\n",
      "             c    d    e    f    g\n",
      "b                                 \n",
      "4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "2.0        3.0  4.0  5.0  6.0  7.0\n",
      "95.0       NaN  6.0  8.0  8.0  8.0 \n",
      "\n",
      "            b       c    d    e    f    g\n",
      "  d                                      \n",
      "C 8.0  4536.0  4536.0  8.0  NaN  NaN  NaN\n",
      "D 7.0  2324.0  2324.0  7.0  NaN  NaN  NaN\n",
      "G 4.0     2.0     3.0  4.0  5.0  6.0  7.0\n",
      "I 6.0    95.0     NaN  6.0  8.0  8.0  8.0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(tmp, '\\n')\n",
    "print(tmp.set_index(['b']), '\\n')\n",
    "print(tmp.set_index(['d'], drop=False, append=True), '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48e56765",
   "metadata": {},
   "source": [
    "##### reset_index\n",
    "\n",
    "重置索引\n",
    "\n",
    "**DataFrame.reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill='')**\n",
    "* level：数值类型可以为：int、str、tuple或list，默认无，仅从索引中删除给定级别。默认情况下移除所有级别。控制了具体要还原的那个等级的索引 。\n",
    "* drop：**当指定drop=False时，则索引列会被还原为普通列；否则，经设置后的新索引值被会丢弃。默认为False。**\n",
    "* inplace：输入布尔值，表示当前操作是否对原数据生效，默认为False。\n",
    "* col_level：数值类型为int或str，默认值为0，如果列有多个级别，则确定将标签插入到哪个级别。默认情况下，它将插入到第一级。\n",
    "* col_fill：对象，默认‘’，如果列有多个级别，则确定其他级别的命名方式。如果没有，则重复索引名。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e46a8562",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              状态  评分  星级     时长\n",
      "电影名                                                 \n",
      "狗神                    nowplaying   8.3   4.5  114分钟\n",
      "三大队                  nowplaying   7.9   4.0  132分钟\n",
      "怒潮                    nowplaying   NaN   NaN  106分钟\n",
      "名侦探柯南：黑铁的鱼影  nowplaying   6.7   3.5  110分钟\n",
      "旺卡                    nowplaying   7.6   4.0  116分钟\n",
      "涉过愤怒的海            nowplaying   7.5   4.0  144分钟\n",
      "瞒天过海                nowplaying   6.3   3.5  105分钟\n",
      "照明商店                nowplaying   6.5   3.5   99分钟\n",
      "爆裂点                  nowplaying   6.5   3.5  138分钟\n",
      "月光武士                nowplaying   NaN   NaN  110分钟\n",
      "饥饿游戏：鸣鸟与蛇之歌  nowplaying   7.0   3.5  158分钟 \n",
      "\n",
      "                    电影名        状态  评分  星级     时长\n",
      "0                     狗神  nowplaying   8.3   4.5  114分钟\n",
      "1                   三大队  nowplaying   7.9   4.0  132分钟\n",
      "2                     怒潮  nowplaying   NaN   NaN  106分钟\n",
      "3   名侦探柯南：黑铁的鱼影  nowplaying   6.7   3.5  110分钟\n",
      "4                     旺卡  nowplaying   7.6   4.0  116分钟\n",
      "5             涉过愤怒的海  nowplaying   7.5   4.0  144分钟\n",
      "6                 瞒天过海  nowplaying   6.3   3.5  105分钟\n",
      "7                 照明商店  nowplaying   6.5   3.5   99分钟\n",
      "8                   爆裂点  nowplaying   6.5   3.5  138分钟\n",
      "9                 月光武士  nowplaying   NaN   NaN  110分钟\n",
      "10  饥饿游戏：鸣鸟与蛇之歌  nowplaying   7.0   3.5  158分钟 \n",
      "\n",
      "          状态  评分  星级     时长\n",
      "0   nowplaying   8.3   4.5  114分钟\n",
      "1   nowplaying   7.9   4.0  132分钟\n",
      "2   nowplaying   NaN   NaN  106分钟\n",
      "3   nowplaying   6.7   3.5  110分钟\n",
      "4   nowplaying   7.6   4.0  116分钟\n",
      "5   nowplaying   7.5   4.0  144分钟\n",
      "6   nowplaying   6.3   3.5  105分钟\n",
      "7   nowplaying   6.5   3.5   99分钟\n",
      "8   nowplaying   6.5   3.5  138分钟\n",
      "9   nowplaying   NaN   NaN  110分钟\n",
      "10  nowplaying   7.0   3.5  158分钟 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "tmp = df.iloc[:11, :5]\n",
    "tmp1 = tmp.set_index(['电影名'])\n",
    "print(tmp1, '\\n')\n",
    "\n",
    "tmp2 = tmp1.reset_index(drop=False)\n",
    "print(tmp2, '\\n')\n",
    "tmp3 = tmp1.reset_index(drop=True)\n",
    "print(tmp3, '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f11b38f",
   "metadata": {},
   "source": [
    "### 排序\n",
    "\n",
    "**DataFrame.sort_values(by, axis, ascending, inplace, kind, na_position, ignore_index)**\n",
    "* by: 要排序的名称列表。\n",
    "* axis: 排列方向\n",
    "  + 0: 按列的值排列 所有行。默认\n",
    "  + 1: 按行的值排列 所有列。\n",
    "* ascending: 升序或降序排序，布尔值。\n",
    "  + True: 升序排序。默认\n",
    "  + False: 降序排序。\n",
    "* kind: 指定排序算法，quicksort, mergesort, heapsort，默认为quicksort。\n",
    "* na_position: 空值的位置，first空值在数据开头，last空值在数据最后，默认last。\n",
    "* ignore_index: 布尔值，是否忽略索引，值为True标记索引，False忽略索引。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "045ca0e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    电影名        状态  评分  星级     时长\n",
      "0                     狗神  nowplaying   8.3   4.5  114分钟\n",
      "1                   三大队  nowplaying   7.9   4.0  132分钟\n",
      "4                     旺卡  nowplaying   7.6   4.0  116分钟\n",
      "5             涉过愤怒的海  nowplaying   7.5   4.0  144分钟\n",
      "10  饥饿游戏：鸣鸟与蛇之歌  nowplaying   7.0   3.5  158分钟\n",
      "3   名侦探柯南：黑铁的鱼影  nowplaying   6.7   3.5  110分钟\n",
      "7                 照明商店  nowplaying   6.5   3.5   99分钟\n",
      "8                   爆裂点  nowplaying   6.5   3.5  138分钟\n",
      "6                 瞒天过海  nowplaying   6.3   3.5  105分钟\n",
      "2                     怒潮  nowplaying   NaN   NaN  106分钟\n",
      "9                 月光武士  nowplaying   NaN   NaN  110分钟 \n",
      "\n",
      "                    电影名        状态  评分  星级     时长\n",
      "0                 瞒天过海  nowplaying   6.3   3.5  105分钟\n",
      "1                 照明商店  nowplaying   6.5   3.5   99分钟\n",
      "2                   爆裂点  nowplaying   6.5   3.5  138分钟\n",
      "3   名侦探柯南：黑铁的鱼影  nowplaying   6.7   3.5  110分钟\n",
      "4   饥饿游戏：鸣鸟与蛇之歌  nowplaying   7.0   3.5  158分钟\n",
      "5             涉过愤怒的海  nowplaying   7.5   4.0  144分钟\n",
      "6                     旺卡  nowplaying   7.6   4.0  116分钟\n",
      "7                   三大队  nowplaying   7.9   4.0  132分钟\n",
      "8                     狗神  nowplaying   8.3   4.5  114分钟\n",
      "9                     怒潮  nowplaying   NaN   NaN  106分钟\n",
      "10                月光武士  nowplaying   NaN   NaN  110分钟 \n",
      "\n",
      "                    电影名        状态  评分  星级     时长\n",
      "0                     狗神  nowplaying   8.3   4.5  114分钟\n",
      "1                   三大队  nowplaying   7.9   4.0  132分钟\n",
      "4                     旺卡  nowplaying   7.6   4.0  116分钟\n",
      "5             涉过愤怒的海  nowplaying   7.5   4.0  144分钟\n",
      "10  饥饿游戏：鸣鸟与蛇之歌  nowplaying   7.0   3.5  158分钟\n",
      "3   名侦探柯南：黑铁的鱼影  nowplaying   6.7   3.5  110分钟\n",
      "7                 照明商店  nowplaying   6.5   3.5   99分钟\n",
      "8                   爆裂点  nowplaying   6.5   3.5  138分钟\n",
      "6                 瞒天过海  nowplaying   6.3   3.5  105分钟\n",
      "2                     怒潮  nowplaying   NaN   NaN  106分钟\n",
      "9                 月光武士  nowplaying   NaN   NaN  110分钟 \n",
      "\n",
      "     g    f    e    d       c       b\n",
      "C  NaN  NaN  NaN  8.0  4536.0  4536.0\n",
      "D  NaN  NaN  NaN  7.0  2324.0  2324.0\n",
      "E  8.0  8.0  8.0  6.0     NaN    95.0\n",
      "F  4.0  4.0  4.0  5.0     NaN    96.0\n",
      "G  7.0  6.0  5.0  4.0     3.0     2.0\n",
      "H  NaN  NaN  NaN  NaN     NaN     NaN\n",
      "I  8.0  8.0  8.0  6.0     NaN    95.0\n"
     ]
    }
   ],
   "source": [
    "tmp1 = tmp.sort_values(by=['评分'], ascending=False)\n",
    "print(tmp1, '\\n')\n",
    "tmp2 = tmp.sort_values(by=['评分'], ignore_index=True)\n",
    "print(tmp2, '\\n')\n",
    "\n",
    "tmp3 = tmp.sort_values(by=['评分', '星级'], ascending=[False, False])\n",
    "print(tmp3, '\\n')\n",
    "\n",
    "print(d.sort_values(by=['G'], axis=1, ascending=False, kind='mergesort'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6973b58",
   "metadata": {},
   "source": [
    "### 排名\n",
    "\n",
    "**rank(method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)**\n",
    "<!-- * axis：表示排名是根据哪个轴，axis=0 表示横轴，axis=1 表示纵轴 -->\n",
    "* method：取值可以为'average'，'first'，'min'， 'max'，'dense'；默认是 average\n",
    "  + average：平均排名\n",
    "  + first：根据排序后的第一个位置的排名\n",
    "  + min：根据排序后的最小位置的排名\n",
    "  + max：根据排序后的最大位置的排名\n",
    "  + dense：相同的数值排名相同，下个数值的排名不出现跳跃\n",
    "* numeric_only：是否仅仅计算数字型的 columns\n",
    "* na_optiaon：空值是否参与排名 以及 如何排名；取值为 keep、top、bottom\n",
    "  + keep：保留空值\n",
    "  + top：空值排名在最前面\n",
    "  + bottom：空值排名在最后面\n",
    "* ascending： 升序或降序排序，布尔值。\n",
    "  + True: 升序排序。默认\n",
    "  + False: 降序排序。\n",
    "* pct：是否以排名的百分比显示排名；所有排名和最大排名的百分比值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "174248ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    电影名  状态  评分  星级  时长\n",
      "0      9.0   6.0   9.0   9.0   5.0\n",
      "1      1.0   6.0   8.0   7.0   7.0\n",
      "2      3.0   6.0   NaN   NaN   2.0\n",
      "3      2.0   6.0   4.0   3.0   3.5\n",
      "4      4.0   6.0   7.0   7.0   6.0\n",
      "5      6.0   6.0   6.0   7.0   9.0\n",
      "6     10.0   6.0   1.0   3.0   1.0\n",
      "7      7.0   6.0   2.5   3.0  11.0\n",
      "8      8.0   6.0   2.5   3.0   8.0\n",
      "9      5.0   6.0   NaN   NaN   3.5\n",
      "10    11.0   6.0   5.0   3.0  10.0 \n",
      "\n",
      "0     9.0\n",
      "1     6.0\n",
      "2     NaN\n",
      "3     1.0\n",
      "4     7.0\n",
      "5     8.0\n",
      "6     2.0\n",
      "7     3.0\n",
      "8     4.0\n",
      "9     NaN\n",
      "10    5.0\n",
      "Name: 星级, dtype: float64 \n",
      "\n",
      "0     9.0\n",
      "1     6.0\n",
      "2     NaN\n",
      "3     1.0\n",
      "4     6.0\n",
      "5     6.0\n",
      "6     1.0\n",
      "7     1.0\n",
      "8     1.0\n",
      "9     NaN\n",
      "10    1.0\n",
      "Name: 星级, dtype: float64 \n",
      "\n",
      "0     9.0\n",
      "1     8.0\n",
      "2     NaN\n",
      "3     5.0\n",
      "4     8.0\n",
      "5     8.0\n",
      "6     5.0\n",
      "7     5.0\n",
      "8     5.0\n",
      "9     NaN\n",
      "10    5.0\n",
      "Name: 星级, dtype: float64 \n",
      "\n",
      "0     1.000000\n",
      "1     0.666667\n",
      "2          NaN\n",
      "3     0.333333\n",
      "4     0.666667\n",
      "5     0.666667\n",
      "6     0.333333\n",
      "7     0.333333\n",
      "8     0.333333\n",
      "9          NaN\n",
      "10    0.333333\n",
      "Name: 星级, dtype: float64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 计算每一列的对应的排名\n",
    "print(tmp.rank(), '\\n')\n",
    "\n",
    "# 计算星级的排名\n",
    "print(tmp['星级'].rank(method='first'), '\\n')\n",
    "print(tmp['星级'].rank(method='min'), '\\n')\n",
    "print(tmp['星级'].rank(method='max'), '\\n')\n",
    "print(tmp['星级'].rank(method='dense', pct=True), '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8461fbe",
   "metadata": {},
   "source": [
    "### 分组\n",
    "\n",
    "**DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, observed=False, dropna=True)**\n",
    "* by：用于分组的列名或函数。可以是一个列名、一个函数、一个列表或一个字典。\n",
    "* axis：分组轴。如果 axis=0（默认值），则沿着行方向分组；如果 axis=1，则沿着列方向分组。\n",
    "* level：在多层索引的情况下，用于指定分组的级别。\n",
    "* as_index：是否将分组键作为索引返回。如果 as_index=True（默认值），则返回一个带有分组键作为索引的对象；否则返回一个不带索引的对象。\n",
    "* sort：是否对分组键进行排序。如果 sort=True（默认值），则对分组键进行排序；否则不排序。\n",
    "* group_keys：是否在结果中包含分组键。如果 group_keys=True（默认值），则在结果中包含分组键；否则不包含。\n",
    "* squeeze：是否压缩返回结果。如果 squeeze=True，则尝试压缩返回结果；否则不压缩。\n",
    "* observed：是否仅使用观察到的类别进行分组。仅适用于类别类型数据。\n",
    "* dropna：是否删除包含缺失值的行。如果 dropna=True（默认值），则删除包含缺失值的行；否则保留。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4e3dcbe3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "星级\n",
      "1.5    2.4\n",
      "2.0    4.1\n",
      "3.0    6.0\n",
      "3.5    7.2\n",
      "4.0    8.1\n",
      "4.5    8.6\n",
      "Name: 评分, dtype: float64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 按照 星级 分组\n",
    "group = df.groupby(by='星级')\n",
    "\n",
    "# 计算每个分组中 评分的最大值\n",
    "max_rating = group['评分'].max()\n",
    "print(max_rating, '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bf87fca",
   "metadata": {},
   "source": [
    "#### agg（aggregate） 聚合操作\n",
    "\n",
    "**DataFrame.agg(func=None, axis=0, \\*args, \\*\\*kwargs)**\n",
    "* func：聚合函数，默认是numpy中的聚合函数。\n",
    "* axis：聚合方向，0表示按行聚合，1表示按列聚合。\n",
    "* *args：其他参数。\n",
    "* **kwargs：其他关键字参数。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5480eba8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     评分           评价人数              \n",
      "     amax       std     amin           var\n",
      "星级                                      \n",
      "1.5   2.4       NaN   4059.0           NaN\n",
      "2.0   4.1       NaN   1166.0           NaN\n",
      "3.0   6.0  0.212132  16917.0  3.640604e+09\n",
      "3.5   7.2  0.287352   2449.0  2.294865e+08\n",
      "4.0   8.1  0.318479   5155.0  2.700215e+10\n",
      "4.5   8.6  0.212132  21292.0  2.620081e+10 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 按照 星级 分组\n",
    "group = df.groupby(by='星级')\n",
    "\n",
    "# 计算每个分组中 评分的最大值和标准差 评价人数的最小值和方差\n",
    "info = group.agg({'评分': [np.max, np.std], '评价人数': [np.min, np.var]})\n",
    "print(info, '\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17f73002",
   "metadata": {},
   "source": [
    "#### transform\n",
    "\n",
    "**transform(func, axis=0, \\*args, \\*\\*kwargs)**\n",
    "* func：必需参数，指定要应用于 DataFrame 的函数。该函数可以接受任意数量的参数，但通常需要至少一个参数，即要应用函数的列或分组。\n",
    "* axis：可选参数，指定应用函数的轴。axis=0 表示应用函数到列，axis=1 表示应用函数到行。默认值为 axis=0。\n",
    "* *args：可选参数，用于传递额外的参数给函数。\n",
    "* **kwargs：可选参数，用于传递额外的关键字参数给函数。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e9da983d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    电影名        状态  评分  星级     时长  平均分数\n",
      "0                     狗神  nowplaying   8.3   4.5  114分钟  8.300000\n",
      "1                   三大队  nowplaying   7.9   4.0  132分钟  7.666667\n",
      "2                     怒潮  nowplaying   NaN   NaN  106分钟       NaN\n",
      "3   名侦探柯南：黑铁的鱼影  nowplaying   6.7   3.5  110分钟  6.600000\n",
      "4                     旺卡  nowplaying   7.6   4.0  116分钟  7.666667\n",
      "5             涉过愤怒的海  nowplaying   7.5   4.0  144分钟  7.666667\n",
      "6                 瞒天过海  nowplaying   6.3   3.5  105分钟  6.600000\n",
      "7                 照明商店  nowplaying   6.5   3.5   99分钟  6.600000\n",
      "8                   爆裂点  nowplaying   6.5   3.5  138分钟  6.600000\n",
      "9                 月光武士  nowplaying   NaN   NaN  110分钟       NaN\n",
      "10  饥饿游戏：鸣鸟与蛇之歌  nowplaying   7.0   3.5  158分钟  6.600000 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "tmp = df.iloc[:11, :5]\n",
    "group = tmp.groupby(by='星级')\n",
    "\n",
    "# 计算对应分组内评分的平均值，形成一列，添加到原表后\n",
    "tmp['平均分数'] = group['评分'].transform('mean')\n",
    "print(tmp, '\\n')\n",
    "\n",
    "# tmp[['最大评分', '最小评分']] = group.transform(lambda x: {\n",
    "#     '最大评分': [x['评分'].max()],\n",
    "#     '最小评分': [x['评分'].min()]\n",
    "# })\n",
    "# print(tmp, '\\n')\n"
   ]
  }
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