diff --git a/README.md b/README.md index cf658ea..7dc2389 100644 --- a/README.md +++ b/README.md @@ -15,6 +15,36 @@ wxapp： +zhihucrawler: + +爬取知乎: + +拿到cookie + +![image-20210105191818870](https://tva1.sinaimg.cn/large/0081Kckwgy1gmd1bx86xqj31ce0tqgz7.jpg) + + + +获取开始链接： + +![Screenshot 2021-01-05 at 16.04.25](https://tva1.sinaimg.cn/large/0081Kckwgy1gmd16l6l5pj30y20u0wlq.jpg) + + + + + +参考仓库： + +https://github.com/visionshao/-zhihu-crawl- + +参考文章： + +https://zhuanlan.zhihu.com/p/78552777 + +获取cookie: + +https://mkyong.com/computer-tips/how-to-view-http-headers-in-google-chrome/#:~:text=In%20Chrome%2C%20visit%20a%20URL,displayed%20on%20the%20right%20panel. + ## Website: diff --git a/zhihucrawler/simsun.ttf b/zhihucrawler/simsun.ttf new file mode 100644 index 0000000..e0115ab Binary files /dev/null and b/zhihucrawler/simsun.ttf differ diff --git a/zhihucrawler/zhihu.ipynb b/zhihucrawler/zhihu.ipynb new file mode 100644 index 0000000..826f390 --- /dev/null +++ b/zhihucrawler/zhihu.ipynb @@ -0,0 +1,557 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### TEST" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "from bs4 import BeautifulSoup\n", + "headers={\n", + "'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36',\n", + "'Referer':'https://www.zhihu.com/question/432856600',\n", + "'Cookie':'_zap=1974916d-1ae4-4bcf-b28d-d027d86657cd; d_c0=\"ABCUnFYZORKPTkBSMV0itEw_0wsgcQa_7hg=|1605849492\"; tst=r; __utmv=51854390.100-1|2=registration_date=20180522=1^3=entry_date=20180522=1; q_c1=b069c444854348549c563ee0d3132d78|1609125177000|1606487257000; _xsrf=ecc0cc60-b14a-4591-800e-ba39349762c9; __utmc=51854390; __utma=51854390.1152446340.1606490164.1609598455.1609828572.14; __utmb=51854390.0.10.1609828572; __utmz=51854390.1609828572.14.14.utmcsr=zhihu.com|utmccn=(referral)|utmcmd=referral|utmcct=/question/29925879; Hm_lvt_98beee57fd2ef70ccdd5ca52b9740c49=1609827347,1609827466,1609828486,1609828575; capsion_ticket=\"2|1:0|10:1609828808|14:capsion_ticket|44:ZDQ0YWRkNjRkZmYzNGQ3Y2E3YWQxMmVhYjhmNGJkMTM=|1d08820ddc8d38c0a018c481754d5ddc00c80d214aa09c87f1b654ea7b0c5a16\"; l_n_c=1; r_cap_id=\"N2ZiM2FmZWJiZTQwNDY5ZTk4NWMyYzZiZmYzNzJhM2Y=|1609828810|ae82c7c57c3624db29f6fc63a59b14ff9c491d6d\"; cap_id=\"MWIxODJlNDIxZDVmNDE2ZGJiZTNmZTQ2NmE3OWJkYzI=|1609828810|2f5742560343f03db0d299bb298801b911982c64\"; l_cap_id=\"ZTBhMGNkZmZmY2ZlNGY1ZGFlNzAzMTAwNGZkMDMxYzY=|1609828810|6599b1dbf24c492bc51f0a5ef3d729a0266e3323\"; n_c=1; z_c0=Mi4xbFZpWUNRQUFBQUFBRUpTY1ZoazVFaGNBQUFCaEFsVk5WMWpoWUFDenF6dG00ZUpxbkZPYVVCOUpPM2xzY1EyN0VR|1609828951|02c73ae04b4f5209ae83d96bd4fbfc2091997d72; SESSIONID=Tdzsm5XMOzualfvbOUJuxIwntTi7RK2aCSH6f3Ix5Yc; 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"坐标北京，女，自身条件，择偶条件如下，为什么遇不到合适的对象？\n", + "42 个回答\n", + "咱们一条一条分析。\n", + "1、88-93，北京人，城六区。\n", + "88-93，对应的高考就是06-11年。\n", + "所以88-93，这6年，同龄人就是62.7万。\n", + "城六区，占62%吧，还有38.87万人。\n", + "男性占51.6%，还有20.05万人。\n", + "2、身高175以上，五官端正，身材匀称。\n", + "北京男性平均身高正好是175。但这不代表满大街都是175的。你可以简单理解为有一个175以上的，那势必会有一个175以下的。\n", + "这个涉及加权平均数，需要各身高数值出现的次数（频数），比较难计算。概率就粗略计算为50%吧，还有10万人。\n", + "五官端正，并不是大帅哥，又有点外协。\n", + "那就是丑的、端正、帅哥分3类，要找其中的端正呗。那概率就算1/3，还有3.3万人。\n", + "身材匀称，（北京人胖子可不少，近26%），那么不胖的就是74%，还剩2.4万人。\n", + "3、一本以上学历，工资要高于女方。\n", + "北京本科录取率大概50%，还剩1.2万人。\n", + "哦对，光本科还不行，您还要求一本以上，那估计概率也就25%了。还剩6000人。\n", + "北京平均工资9000，要超过女生1W，这个类似身高的加权平均数，那概率也50%，还剩3千人。\n", + "4、忘记了一点，单身啊。\n", + "最小93年，27岁了。\n", + "满足以上条件的，27岁-32岁，没结婚，且没有对象的。你说还剩多少？\n", + "给你往多了算算，剩一半，行吧。最后还有1500人。\n", + "5、杂七杂八的\n", + "你也说了，有人介绍，性格不合你朋友也不满意。\n", + "把性格啊，三观啊。爱好啊，这些杂七杂八的不满意的给你淘汰了，给你淘汰1/3，还剩下多少人。\n", + "1000人！\n", + "北京常驻人口多少，2000多万人。\n", + "您在茫茫人海，以两万分之一的概率，找这么个男人。\n", + "你觉得难不难？\n", + "PS:拿我自己举个例。我对照一下。\n", + "我91年，180，五官端正，本科，月入2万，有车有房。\n", + "不符合的地方，北京郊区的，二本，胖子。\n", + "而我谈过3个女朋友都是外地的，\n", + "她们全是研究生毕业，长得漂亮。\n", + "我有车有房，找北京姑娘和外地姑娘有什么关系？\n", + "而且，找北京姑娘人家还看不上我，又郊区又胖，哈哈哈。\n", + "所以你们看不上别人的同时，别人也不见得非得找你们。\n", + "你自己也说了，亲戚朋友介绍的对象，家人都觉得不是什么大问题，你还不是很满意。那你就好好单着呗。\n", + "知足者常乐。\n", + "接着你修改的补充一下\n", + "这个依旧没有任何意义，所谓三个最在意的作为硬线，只不过是加了个最低下限。\n", + "最低要求60分，但是她依旧择优录取，所以这个最低要求有什么意义？\n", + "遇到65分的，可她还是觉得下一个会更高，这有什么实质性的意义？\n", + "这玩意充其量是拿来自欺欺人，掩饰自己。让别人看起来，觉得“我不挑”“我要求低，只有3点硬性要求”，哈哈哈简直自作聪明。\n", + "真想找对象，设了3个硬性要求，对方符合了，那就和人家好好处，别再挑三捡四了。人无完人。\n", + "一个人，恋爱前后可能判若两人，有很多问题，你不相处也发现不了，同理，你真心相处了，可能很多问题也不是问题了。\n", + "最后说一句，\n", + "你所谓的帮你朋友咨询，其实只不过就是在反向寻求认同。\n", + "任何与你们相反的观点，都可以找各种借口和理由解释，其实没必要。掩耳盗铃，自欺欺人而已。\n", + "本身事情就都是两面性，没有对错，无非看理解。\n", + "只要你朋友，自己真的问心无愧，自己真的过得开心，那她说的做的一切，对她来说都是对的。\n", + "当然，一个人到底真的开不开心，只有她自己知道。\n", + "所以没必要在别人那里寻求认同感，自己无悔就好。\n", + "\n", + "(눈_눈)这个妹子条件可以说想当高\n", + "下面是妹子原文。\n", + "我朋友北京土著女，93年，事业稳定，有时间休息，收入稳定1W+，狮子座。家里有房有车，父母有退休金和医保，养老不用愁。长相也还行，打5分吧，身高172，皮肤很白，身材匀称。\n", + "27岁年龄较大而且一般女方需要男方是城6区有房。。事业稳定1w多。这个太厉害单稳定月薪1w以上基本就pass程序员这种了。我是庸人，知道有这收入的只有4种行业。警察(我认识个朋友好像是五道口职业学院毕业的)。医生。各大行工程师(我只认识一个复旦毕业的)。以及央企/国企小领导。。。。如果算地域。。北京考生能进去的。这岁数还没结婚性格还得好这种人还有多少。。其次房子问题，你想找学区房我就替你直说了吧。。后面孩子教育问题肯定必须的。一套1500w左右\n", + "\n", + "性格上外冷内热，有点刀子嘴豆腐心，聊天时嘴有点损，喜欢怼人，不过其实熟悉了就知道是个铁憨憨。从来没有谈过恋爱。\n", + "生活上比较精致，吃穿用都是名牌。基本上衣服和护肤品用的都是四位数的。养了一条萨摩耶。\n", + "妹子没必要提这种花钱爱好，家底2000w以上。事业足够稳定甚至部分是央企领导岗的男孩需要的是一个温柔可人知冷知热。气质极佳的妹子。4位数衣服。。。对于这种收入的家庭一起聚会有可能还穿不出去。\n", + "她有一点外貌协会，所以见了好多男生都没啥感觉。但是陆陆续续找了三年了，亲戚朋友也介绍了好多男生给她，不过最后都因为各式各样的原因没成。有拒绝别人的，也有被别人拒绝的时候。\n", + "看来妹子还特别重视男方外貌和soul。。嗯。所谓弱水三千只取一瓢。找灵魂中命定的白马王子。。。难度真的不是一般的大。。。不过还是希望妹子找到真爱吧\n", + "择偶标准如下：\n", + "88-93，北京人，身高175以上，五官端正，身材匀称（不要胖子），一本以上学历，最好城六区，北京市人均工资以上，要高于女方。\n", + "求大家帮忙看看，我们都觉得这个标准男生应该不难达到\n", + "总结下。我是庸人，按我的理解妹子的要求男方必须北京原土著。可以稳定到退休的工作(程序员这种华为阿里我认为短时间收入还不错应该可以其他的不了解)，目前月薪1-2w且有升职空间。家有至少一套学区房+任意一套城6区住房。对男方外貌要求很高，很注重打扮和着装。同时要是soulmate.能忍受妹子的各种暴脾气照顾她。懂她。\n" + ] + } + ], + "source": [ + "# drawbacks: 只能爬取前面几条\n", + "soup = soup.find_all([\"p\",\"h1\",\"h2\",\"h3\",\"h4\",\"h5\",\"h6\",\"h7\"])\n", + "\n", + "\n", + "for item in soup:\n", + " print(item.get_text().strip())" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "running.\n", + "answer 1 : 咱们一条一条分析。1、88-93，北京人，城六区。88-93，对应的高考就是06-11年。所以88-93，这6年，同龄人就是62.7万。城六区，占62%吧，还有38.87万人。男性占51.6%，还有20.05万人。2、身高175以上，五官端正，身材匀称。北京男性平均身高正好是175。但这不代表满大街都是175的。你可以简单理解为有一个175以上的，那势必会有一个175以下的。这个涉及加权平均数，需要各身高数值出现的次数（频数），比较难计算。概率就粗略计算为50%吧，还有10万人。五官端正，并不是大帅哥，又有点外协。那就是丑的、端正、帅哥分3类，要找其中的端正呗。那概率就算1/3，还有3.3万人。身材匀称，（北京人胖子可不少，近26%），那么不胖的就是74%，还剩2.4万人。3、一本以上学历，工资要高于女方。北京本科录取率大概50%，还剩1.2万人。哦对，光本科还不行，您还要求一本以上，那估计概率也就25%了。还剩6000人。北京平均工资9000，要超过女生1W，这个类似身高的加权平均数，那概率也50%，还剩3千人。4、忘记了一点，单身啊。最小93年，27岁了。满足以上条件的，27岁-32岁，没结婚，且没有对象的。你说还剩多少？给你往多了算算，剩一半，行吧。最后还有1500人。5、杂七杂八的你也说了，有人介绍，性格不合你朋友也不满意。把性格啊，三观啊。爱好啊，这些杂七杂八的不满意的给你淘汰了，给你淘汰1/3，还剩下多少人。1000人！北京常驻人口多少，2000多万人。您在茫茫人海，以两万分之一的概率，找这么个男人。你觉得难不难？PS:拿我自己举个例。我对照一下。我91年，180，五官端正，本科，月入2万，有车有房。不符合的地方，北京郊区的，二本，胖子。而我谈过3个女朋友都是外地的，她们全是研究生毕业，长得漂亮。我有车有房，找北京姑娘和外地姑娘有什么关系？而且，找北京姑娘人家还看不上我，又郊区又胖，哈哈哈。所以你们看不上别人的同时，别人也不见得非得找你们。你自己也说了，亲戚朋友介绍的对象，家人都觉得不是什么大问题，你还不是很满意。那你就好好单着呗。知足者常乐。接着你修改的补充一下现在我朋友受到了大家的启发，从自己的标准中选择了三个最在意的作为硬线：北京人，不胖，月薪过万。而其他的作为加分项，与人相亲时也多用加分评判别人~这个依旧没有任何意义，所谓三个最在意的作为硬线，只不过是加了个最低下限。最低要求60分，但是她依旧择优录取，所以这个最低要求有什么意义？遇到65分的，可她还是觉得下一个会更高，这有什么实质性的意义？这玩意充其量是拿来自欺欺人，掩饰自己。让别人看起来，觉得“我不挑”“我要求低，只有3点硬性要求”，哈哈哈简直自作聪明。真想找对象，设了3个硬性要求，对方符合了，那就和人家好好处，别再挑三捡四了。人无完人。一个人，恋爱前后可能判若两人，有很多问题，你不相处也发现不了，同理，你真心相处了，可能很多问题也不是问题了。最后说一句，你所谓的帮你朋友咨询，其实只不过就是在反向寻求认同。任何与你们相反的观点，都可以找各种借口和理由解释，其实没必要。掩耳盗铃，自欺欺人而已。本身事情就都是两面性，没有对错，无非看理解。只要你朋友，自己真的问心无愧，自己真的过得开心，那她说的做的一切，对她来说都是对的。当然，一个人到底真的开不开心，只有她自己知道。所以没必要在别人那里寻求认同感，自己无悔就好。\n", + "answer 2 : (눈_눈)这个妹子条件可以说想当高下面是妹子原文。我朋友北京土著女，93年，事业稳定，有时间休息，收入稳定1W+，狮子座。家里有房有车，父母有退休金和医保，养老不用愁。长相也还行，打5分吧，身高172，皮肤很白，身材匀称。27岁年龄较大而且一般女方需要男方是城6区有房。。事业稳定1w多。这个太厉害单稳定月薪1w以上基本就pass程序员这种了。我是庸人，知道有这收入的只有4种行业。警察(我认识个朋友好像是五道口职业学院毕业的)。医生。各大行工程师(我只认识一个复旦毕业的)。以及央企/国企小领导。。。。如果算地域。。北京考生能进去的。这岁数还没结婚性格还得好这种人还有多少。。其次房子问题，你想找学区房我就替你直说了吧。。后面孩子教育问题肯定必须的。一套1500w左右性格上外冷内热，有点刀子嘴豆腐心，聊天时嘴有点损，喜欢怼人，不过其实熟悉了就知道是个铁憨憨。从来没有谈过恋爱。生活上比较精致，吃穿用都是名牌。基本上衣服和护肤品用的都是四位数的。养了一条萨摩耶。妹子没必要提这种花钱爱好，家底2000w以上。事业足够稳定甚至部分是央企领导岗的男孩需要的是一个温柔可人知冷知热。气质极佳的妹子。4位数衣服。。。对于这种收入的家庭一起聚会有可能还穿不出去。她有一点外貌协会，所以见了好多男生都没啥感觉。但是陆陆续续找了三年了，亲戚朋友也介绍了好多男生给她，不过最后都因为各式各样的原因没成。有拒绝别人的，也有被别人拒绝的时候。看来妹子还特别重视男方外貌和soul。。嗯。所谓弱水三千只取一瓢。找灵魂中命定的白马王子。。。难度真的不是一般的大。。。不过还是希望妹子找到真爱吧择偶标准如下：88-93，北京人，身高175以上，五官端正，身材匀称（不要胖子），一本以上学历，最好城六区，北京市人均工资以上，要高于女方。求大家帮忙看看，我们都觉得这个标准男生应该不难达到总结下。我是庸人，按我的理解妹子的要求男方必须北京原土著。可以稳定到退休的工作(程序员这种华为阿里我认为短时间收入还不错应该可以其他的不了解)，目前月薪1-2w且有升职空间。家有至少一套学区房+任意一套城6区住房。对男方外貌要求很高，很注重打扮和着装。同时要是soulmate.能忍受妹子的各种暴脾气照顾她。懂她。\n", + "answer 3 : 要求不高，但很难找，因为符合她这个要求的男人选择面太大了。北京男生可以找外地女生，毕竟外地女生在京有房有户口的也很多很多；175+男生可以找165女生，未必非得找个子高的；一本男生可以找二本女生；一万以上月收入可以找收入少甚至暂时没收入的年轻女生。很多女生都问：为何我要求不高还是找不到，因为所有女生想找的都是同一类男生。而女生的收入，消费习惯，养狗爱好，其实不是什么加分项，甚至是减分项。\n", + "answer 4 : 以下只基于我个人的观点，见识有限，可能不全面：你朋友这个要求是矛盾的。我认识的，北京人基本没有太上进的，学校都是几个二本，工作上也是基于凑活的态度。原因，这个年龄，如果是北京人，应该赶上了好时候，比较有钱（拆迁发展就不说了），也有见识（当年留学浪潮北京肯定有影响），如果凑巧还挺有头脑的话（一本意味着脑子好使，即使北京也不是人人都能考上的），都出国留学发展了，一个个在国外混的都不错。男生都追求奋斗的，也就不回来了。能留下来的，都是甘于平淡的普通人，就造成这种情况了。我一北京朋友，大概二本，相亲时见的他同学，当年高中班级前列，北航毕业。最后确实没成，但当时女生本人是愿意发展的。北京的优秀本地女孩不少，如果要求本地男孩的话，学历贬值严重。至少80%是这样的，剩下的，有人可能智商好一点，有人可能奋斗心强点，但对你朋友的要求，都或多或少不满足。建议抓大放小，看清主要需求，要头脑可以找个外地清华男，要家境可以找个本地拆迁户，要事业可以找个智商在线，情商高的小老板/公务员等等。\n", + "answer 5 : 不知道怎么会邀请到我。大龄女大龄男不都是这么剩下的么。。有个相亲的段子：厕所有三个位置，看第一个坑没有纸，第二个坑有屎，第三个坑没有纸又有屎，回来再看第一个坑，已经有人了。\n", + "answer 6 : 这还不容易解释吗，要求太高了呗，用简单的数学计算就能搞清楚，北京有户口的人就算一千五百万吧，女人占一半好了，男人还有七百五十万，还规定了88年到93年这个年龄段，恐怕连男人总数二十分之一都达不到，就算三十二万人吧。而且岁数都一大把了，别说结婚了，大部分孩子都有了，单身没有女朋友的，离异的估计你朋友也不能考虑，那就剩下十分之一就不错，七万二千人吧，身高又冒出来了，北京的男性平均身高恐怕很难达到175，176，比你朋友高的暂且算一半吧，那就是三万六千人，北京月薪上万人很多，但是也绝到不了普遍，大部分人还是达不到的，我们姑且大胆定为三分之一的人都能过万，那么还剩下一万两千人，我们再善良一点，把五官端正放宽，剩下一万人是长得不错的。这一万人咱们再善良点，八成都住在城六区，那就是八千人，这八千人里刨掉喜欢比自己年纪大的，刨掉喜欢娇小玲珑的，刨掉喜欢二十五岁以下的，就算只刨掉一千人，也只剩下七千人了。你想吧，在茫茫人海一万多平方公里的地面上，在七千人里挑个你看的好，他还喜欢你，得多难，你还基本都不认识，要靠介绍，这七千人里还没计算富二代呀，渣男呀，赌徒呀，流氓呀等等这些可能性呢，你说难不难，真的是灰常难。最后更要命，你能肯定仅剩的小伙儿子（这岁数当小伙儿都勉强了）们就能有房吗，一个房子又得扫倒一片，最后能给你选的不过千八百个，还可能因为吃饭口味，言谈举止，消费习惯被你朋友pass掉，能满意的我认为绝不超过二千人，偌大的北京，你得有多少渠道和人脉能把这两千人推送到这位姑奶奶面前呀，缘分呀！\n", + "answer 7 : 首先你朋友的一大硬伤就是没谈过恋爱，其实出去问，很少有女生会觉得自己性格不好吧？但是没恋爱过的女生真的会有些偏执，她要求的男生不是没有，而是这样的男的选择面太广了，人家土著男家庭不错的，还175，好一点本科以上的全国去北京的妹子都可以挑选，人家只要长相或者情商高一些，根本没有你朋友什么事了，所以建议就是要么提高一下自己情商，去赶紧谈谈恋爱。其次下择，可能可以找到匹配的，比如身高或者土著或者经济方面调一下，不是你朋友要求高，她的要求不过分，过分的是但凡是个单身妹子，要求都是她这个。。。。不信你可以去身边问问，全部都是身高175以上，学历工作Ok，收入高于自己，家庭无短板，性格三观好，关心老婆等等，这样的男的挑选面更广，所以女生要想高择到他们，外在都是次要的了，情商往往最重要，毕竟钱也不缺，能开心的在一起才是关键，所以由于你朋友没有恋爱经验，比较吃亏，最好的选择就是放宽一下条件了，祝好。\n", + "answer 8 : 看女孩情况有这样的择偶标准不算高，但完全符合的男孩确实难碰，我认识的朋友有个跟这个标准接近，94年人家住石景山，房车都有现在自己做生意，但他老人家特别佛不想结婚，以后也不想要孩子，重点是周边女孩挺多，各种类型想找挺容易……还是靠亲戚朋友介绍吧，周边已婚这种居多，要么就是从小认识的，把从小到大的同学及同学周边挖掘一下。另外就是稍微主动点，这个条件的男孩不太愁找女朋友，一旦遇见合适的别错过。\n", + "answer 9 : 要求北京土著这点可以理解，但是也不要太强求。我也帮人介绍过，确实这种会不太好找，因为北京土著真的不多，北京土著没结婚的少，北京土著没结婚还会参加相亲的更少，北京土著没结婚还会参加相亲的我还认识的更加少。\n", + "answer 10 : 第一个，别人给介绍的那些男士，是否符合以上的标准，如果符合了，那为什么又不行了？第二个，自身家庭过度稳定。纯猜测。这和我身边的朋友一样，一家人其乐融融，完全是自己的生活逻辑，很幸福。但是带来的一小点问题是，这样只能要求对象来接纳这种模式，甚至有时候也不太想去改变这种状态。最后的结果就是，如果没有一个人能一开始就融入他的生活模式，那就会让他感觉很累合不来。第三个，买多贵的多精致的都没事。我猜测她也喜欢有趣的男性吧。从我身边来看，但凡家庭资产超过1亿的朋友，都不喜欢张口闭口全是品牌这些话题的，至于着装当然需要有一些套装，但日常应该用其他的单品来体现出自己的着装品味。如果不是那么有趣的但是也很富有的朋友，就会觉得花钱就能稳定住的妹子是很好的了。以上。有很多臆测，如果不准确不要带入。纯属个人一点想法。\n", + "answer 11 : 时机未到！\n", + "answer 12 : 因为她相亲时不止考虑底线那么简单，考虑了更多因素来知乎相亲吧相亲吧！https://www.zhihu.com/club/1254196305989505024\n", + "answer 13 : 其实很多事情道理是互通的，感觉找对象跟找工作特别像。不取决于你自己什么条件，也不取决于你要求什么条件，甚至跟对方的要求也没什么太大的关系。取决于跟你竞争的人是什么条件！也就是说要么同等要求的人里，你能提供的价值最多。要么能提供同等价值的人里，你要求的最少。回到问题这个例子：你这个朋友要求对方长得帅（其他的要求先忽略）那么1.在要求对象帅的适婚女性群体里，你这个朋友能提供的价值是最多的嘛？比方说特别漂亮、特别善解人意、特别温柔、特别精通厨艺等等。或者2.在自身条件相同的适婚女性群体中，你这个朋友要求是最低的。其他人都要求“高富帅”，你这个朋友只要求“帅”，不要求“高”和“富”？所以问题出在哪了，很简单了吧！实话实说的话就是：核心竞争力不足，要求还不低！\n", + "answer 14 : 哈哈，其实要求不高，也不难找，不过我建议把要求不要订那么死，差不多就行了，遇到一个眼缘的也不容易，条件过得去就好\n", + "answer 15 : 作为一个三四线城市的我来看这个觉得题主上面写的要求有点高了北京人一点，就pass很多再加上后面的学历，人均工资，五官端正+身材匀称+175以上讲真一个条件满足的人很多全部都符合，那就很难讲了\n", + "answer 16 : 我基本上符合吧哈哈哈哈，但是我在通州上班，超出城六区范围了但是据我所知这个条件都符合，并且能被认识的，确实太少了起码我自己的初高中同学里，符合条件的基本都结婚了，离婚的也有，离了再结的都有这个要求稍微有点不太适应大环境，本地的其实也不那么太有钱，四位数的穿用有点压力，毕竟月入1w感觉有点需要家里帮衬，当然家里就这么一个小公主也没得褒贬；外地的有钱人不一定有房有车，并且家底儿很难鉴别（因为认识一些很有钱的消费水平不高，家庭条件一般的反而大手大脚）。有钱的就不太在意女生能挣多少钱，主要看颜值身材性格了；没钱的就怕冲着房子车子不是真爱。所以想来想去，就得在标准上做出一些让步。话说回来，这种条件，追着介绍对象的估计海了去了，可能也会发现符合条件的很少吧。\n", + "answer 17 : 主要还是纯北京人太少了吧？我一北京同事，也是打小胡同里长大的北京土著，一块儿长大的发小…有男有女，基本最后结婚的是北京土著的，凤毛麟角。大多都妥协了，主要还是合适的碰着的真不多。想想北京上千万人，本地土著才能有多少？多半是没谈过，所以在这方面自己的要求和期待都会多一些。被毒打过了也就能降低一些标准了。比方说，我认识的北京人，长的精神的真没几个，有的那么几个都早恋早早结婚了都。条条框框越多越不容易碰到合适的人呀适当剔除几项非必要条件吧亲朋好友介绍的相亲，质量基本上都是随着年龄逐步下滑的。（虽然也保不齐什么时候给你来个暴击，一见就特别合适，但几率太小了）这是个悲伤的事实如果实在不愿意妥协，那就单着等呗，或许缘分就在转角呢？\n", + "answer 18 : 貌似不高就是难找，那看看我要求这么低，为啥也找不对象。路人级别即可，身高158-168之间都可以，家境可以低我两个档次，学历不限最好专科以上，女的，性格啥的不是绿茶即可，收入2000+。为什么找不到，原因范围小，性格问题（过于内敛）。\n", + "answer 19 : 要么长得丑，又高又丑是属于特别难找对象的那一类女人。她要求的男的，北京人，工资1万以上，北京城六区内有房产，175以上，还外貌协会要求男的长得好，不能胖，这样条件的男人基本上是在整个中国普通人中的完美条件了，这样的男人找对象是可以要求女方长得好看，同时条件还不错的。而题主描述的朋友条件上是还不错的，那么3年相亲下来的男人，不是不满足条件看不上，就是满足条件被男方拒绝，这种情况下，唯一的答案就是长得丑啦，又高又丑的女孩子确实不太好找对象的。至于说女方性格上有缺陷的话，这点我觉得不可能所有相亲对象第一次见面就能发现的，所以这条不太成立，所以只能是长得丑啦。\n", + "answer 20 : 哎，亲，您都邀请我两回了。。。。。。好尴尬。您这位朋友，自身很优秀，乍一看要求也不高，但是，其中那条，不要求大帅哥，就五官端正就行，看到这条我差不多就能理解了，我觉得她找不到，可能是符合其他条件的，脸或气质不喜欢，脸或气质合格了，其他条件不符合。划重点吧，我觉得您这位朋友多少有些颜控，气质控，年纪不是很小了，找的对象基本比自己还大，但那个年龄段的男生脸和气质都还好的比较少了，早就被别人挑走了，留下的也可能是有各种各样的原因耽误了，再加上其他附加条件，就不太容易找到了。建议你的朋友，要么来个姐弟恋，找小鲜肉，同时别太强求对方经济条件，要么别的条件符合，长相顺眼就试试接触接触，不能两全的情况下，有趣的灵魂还是胜过美丽的脸的。当然，您的朋友最后也许会找到全部符合条件的对象，毕竟缘分这东西如此的玄，谁也说不好什么时候遇到Mr.right～个人想法，不喜勿喷，喷也没用。\n", + "answer 21 : 不要听一些人乱说，30岁左右的男生，当地人，月入1万以上，也没提男生要有单独住房，这要求一点不高，可以说是非常非常普通。这样条件的两个人结合其实在北京也就是中等偏下，未来的婚姻生活必然是俩人月光，蹭父母的房子住，孩子将来上补习班可能都需要双方老人补贴点。难点应该是外形问题，女生长相只有5分（对自身条件还过得去的男生来说，这个颜值其实是偏低了），但又是颜控，显然还处于有点少女心的状态，这就比较难了，长相不太好的女生要找长相周正的男生，这反过来了。如果是今年-明年的话，建议降低对男生颜值的要求（其实再过两三年，估计这个要求自然会降低，30岁长相一般的女生不会再有信心提这个事），提高对收入的要求，比如到2万这个区间，可能反而会容易点，可以搏一下。再过两三年的话，那就再把要求都往再降一档吧。\n", + "answer 22 : 啧啧啧啧，我居然会受邀请这个问题。88-93，32-2730以后的基本都结婚，30以下的可能还在为你这个目标奋斗吧。\n", + "answer 23 : 不改变就等着继续问吧\n", + "answer 24 : 不知道为什么邀请我。“88-93，北京人，身高175以上，五官端正，身材匀称（不要胖子），一本以上学历，最好城六区，北京市人均工资以上，要高于女方。”我也想要这样的对象呢\n", + "answer 25 : 啥条件？\n", + "answer 26 : 相对你朋友条件，要求确实不能算高。但是，男生可以向下找，只要喜欢，经济差点没啥；女生倾向向上找，特别你朋友这种，经济条件不俗的同时，长相也不能差。这种男生选择面可太广了。作为富裕家庭的男生，当然首选有颜有才的，家庭条件好坏不是首要条件。打扮打扮颜值五分，外在条件一般，只能提升内在，让自己变成一个有趣的姑娘，那样才能提高概率。一个富裕姑娘，非要找条件比自己好的。这时候，她的富裕不是助力，而是阻力。女生也可以向下找啊，以你朋友的实力找个有上进心人品端正的精神小伙还是不难吧，自己啥也不缺了，又为啥非要苛求经济条件呢。找对象嘛，最重要是处着舒服！\n", + "answer 27 : 是城六区的也拆出去了不过讲真跟北京您这个条件的属于不上不下的人家找个可心的外地小姑娘也挺舒服还能稍微轻上点儿不是您要求高北京爷们儿是真随遇而安您这要求的基本都是“好奔饬”那一类的我身边这类人基本本科就出国了或者研究生出国反正没在拘泥于大北京另外我不清楚具体她是哪儿忍不了但是既然找不着对眼儿的人还不如要求低点儿说不好听的“忒难伺候”咱就别再可着这要求跟这小池子里霍拢了换个大池子好好挑挑碰上了也就碰上了其实罗列了那么多也就一句话“有上进心知足有责任心门当户对”就结了就是为个共同语言和差不多的三观拽那些量化的指标儿没用\n", + "answer 28 : 局限于一个圈层本来就很难。\n", + "answer 29 : 符合条件的男的，不会找你朋友这样的\n", + "answer 30 : 不是北京的路过\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "answer 31 : 88-93，北京人，身高175以上，五官端正，身材匀称（不要胖子），一本以上学历，最好城六区要求绝对高身高175以上，五官端正，身材匀称（不要胖子）=秒杀90%的颜值和身材北京人一本以上学历，最好城六区=秒杀95%男生概率预期0.05*0.1=0.005一百人中没有1个概率太低实际概率可能还要乘以0.1千人挑1\n", + "answer 32 : solo27年，如果是真的solo，那考虑一下是不是自己性魅力有问题。如果期间有性伴侣，但是基于某些原因没公开，考虑是不是要求过高。比如高富帅睡你但是不想和你确定关系。总而言之，单身27年，还是女生，还是土著看你自己表述外贸条件还行，真的很反常了\n", + "answer 33 : 不知道你考虑过竞争问题吗？特别是北京本地人。在北京的剩女八十万，可是剩男只有十几万。（具体忘了）这些女生都想找个本地北京男。既然你说这个条件的北京男好找的话可以想一下竞争问题，本身北京本地男的选择就很多，现在的情况是条件还可以的北京男都在被哄抢。不好找\n", + "answer 34 : 这要求高个锤子，又没要求爱情。\n", + "answer 35 : 强答一番。你朋友单身的原因其实很简单，符合她要求的人少呗。我们来捋一捋她的要求：1.88-93，北京人。2.身高175以上，五官端正（指的是五官端正的普通人！并不是大帅哥朋友们！），身材匀称（不要胖子）3.一本以上学历。4.最好城六区，北京市人均工资以上，要高于女方。你以为这就完了吗？并不是，因为有一些隐形要求并没有写出来。5.没结过婚。（这一点没提过，但是基本上大家都懂。妹子连恋爱都没谈过，除非对象真的优质王老五，否则怎么会被接受？）6.不谢顶，打扮潮流（至少不土气）。从上文题主提的第二点可以看出，他大概是对于颜控有点误解。诚然，有些小帅哥真的靠着一张脸就能把地摊货穿出时装周效果，但这种人毕竟是极少数。人靠衣装马靠鞍，尤其是当选择范围指定到了从27到32岁之间。别看许多三十岁的男明星依然粉嫩，但对于普通男人而言，三十岁已经开始陆续出现中年征兆了。比如发福，比如谢顶。我猜，你朋友对于相亲过的码农们评价都不高吧？7.家里能够提供资金在北京另购房子，又或者祖传房子够大的。对不起，这是我空口白话凭空想象无中生有暗渡陈仓的污蔑之词。但，但是。我觉得，一个在北京月入过万的男生，哪怕是有北京户口，要考虑着未来房贷车贷聘礼婚礼开销未来小孩读书费用等等压力的男生，无论如何也无法做到如题主朋友一样轻松惬意地买高档消费品，又是养狗又是旅游的。那位女生看似要求工资不高。但是她的生活质量却远远不是一万工资出头的男生能供给得起的。北京户口很难得，但比起在北京买房子的难度又如何？北京本地人每一个都能轻松地在北京再买多一套房子吗？最终还不是得靠家里资金支持或者自己拼搏个世界出来。同样是月入过万，住家里的女生可以赚一万花九千块。而家里没有资金支持的本地男生，就得为自己的未来打算，月入过万都得紧巴巴地过日子。别说女生看不看得上，男生们看到女生这种花钱方法都会知难而退了。所以题主说的相亲第四点，并没有什么实际意义。赚钱多少不重要，重要的是有没有钱。（悄悄哔哔句，如果你的朋友要求真的是她提的那前四条的话，广大的码农斗士们应该都能入选。）\n", + "answer 36 : 一次没恋爱过的明显不会懂怎么爱一个人和付出很多都是妈宝女恋爱完全靠想象就算条件我都符合我压根不会想去认识的\n", + "answer 37 : 如果永远盯着自己身上“闪光点”，那大概率永远找不到。多看看自己有啥不好的地方，然后再找对象就容易多了。每个人的灵魂都有一个缺口，你需要做的应该是用自己的缺口去找另一个可以契合的灵魂。然后才能结合，才称完美。\n", + "answer 38 : 条件不高，门当户对，问题是这样的男生太抢手了，能剩下的都是凤毛麟角\n", + "answer 39 : 从条件来看，相对还是比较匹配的，年龄也不算超标。建议检查一下个人性格、为人处事习惯以及生活圈子。\n", + "answer 40 : 要求门当户对很正常啊。怎么就要求高了？\n", + "answer 41 : 泻药，我88的，身高191，北京土著城六区，我名有两套房，父母名下两套，都在四环边上，有辆45w+的代步车，月入税后2W9+。我觉得咱俩挺合适的。不过我去年结婚了，否则咱俩可以聊一聊。\n", + "answer 42 : 你说的都是客气话，我给大家翻译翻译。首先是女方条件：我朋友北京土著女，93年，事业稳定，有时间休息，收入稳定1W+，狮子座。家里有房有车，父母有退休金和医保，养老不用愁。长相也还行，打5分吧，身高172，皮肤很白，身材匀称。性格上外冷内热，有点刀子嘴豆腐心，聊天时嘴有点损，喜欢怼人（开玩笑那种怼怼的方式~不是吵架脾气大不要误会~），不过其实熟悉了就知道是个铁憨憨。从来没有谈过恋爱。生活上比较精致，吃穿用都是名牌。基本上衣服和护肤品用的都是四位数的。养了一条萨摩耶。她有一点外貌协会，所以见了好多男生都没啥感觉。但是陆陆续续找了三年了，亲戚朋友也介绍了好多男生给她，不过最后都因为各式各样的原因没成。有拒绝别人的，也有被别人拒绝的时候。翻译如下：女，27岁，北京土著，有一份清闲工作，工资不高。跟父母住一起，长相一般，身材一般，身高较高。性格内向，母胎solo27年，直女性格。颜控，相亲3年未成功。择偶标准：88-93，北京人，身高175以上，五官端正（指的是五官端正的普通人！并不是大帅哥朋友们！），身材匀称（不要胖子），一本以上学历，最好城六区，北京市人均工资以上，要高于女方。人话：真的只有这些吗？后面你写的有符合标准的又没看上，那你的真实标准是？猜想：本地土著男性，高(175以上)，帅（五官端正的普通人？不就是帅哥吗），富（一本学历以上，人均工资以上），以上3条需同时满足，缺一不可。我的心里话：劝女方耗子尾汁，适当降低标准，市场已经给你反馈了，怎么办看你自己喽。\n", + "total answers: 42\n" + ] + } + ], + "source": [ + "import requests\n", + "from bs4 import BeautifulSoup\n", + "import json\n", + "import re\n", + "REG = re.compile('<[^>]*>')\n", + "\n", + "\n", + "def extract_answer(s):\n", + " temp_list = REG.sub(\"\", s).replace(\"\\n\", \"\").replace(\" \",\"\")\n", + " return temp_list\n", + "\n", + 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capsion_ticket=\"2|1:0|10:1609828808|14:capsion_ticket|44:ZDQ0YWRkNjRkZmYzNGQ3Y2E3YWQxMmVhYjhmNGJkMTM=|1d08820ddc8d38c0a018c481754d5ddc00c80d214aa09c87f1b654ea7b0c5a16\"; l_n_c=1; r_cap_id=\"N2ZiM2FmZWJiZTQwNDY5ZTk4NWMyYzZiZmYzNzJhM2Y=|1609828810|ae82c7c57c3624db29f6fc63a59b14ff9c491d6d\"; cap_id=\"MWIxODJlNDIxZDVmNDE2ZGJiZTNmZTQ2NmE3OWJkYzI=|1609828810|2f5742560343f03db0d299bb298801b911982c64\"; l_cap_id=\"ZTBhMGNkZmZmY2ZlNGY1ZGFlNzAzMTAwNGZkMDMxYzY=|1609828810|6599b1dbf24c492bc51f0a5ef3d729a0266e3323\"; n_c=1; z_c0=Mi4xbFZpWUNRQUFBQUFBRUpTY1ZoazVFaGNBQUFCaEFsVk5WMWpoWUFDenF6dG00ZUpxbkZPYVVCOUpPM2xzY1EyN0VR|1609828951|02c73ae04b4f5209ae83d96bd4fbfc2091997d72; SESSIONID=Tdzsm5XMOzualfvbOUJuxIwntTi7RK2aCSH6f3Ix5Yc; JOID=VFscA03JGi0ZyfKuTM8v8tKd7ZJRmHRebKTC_AiGZVJxo5TdJFIT1kDM_a1D0TEbwulXAQpCWio09aDi6PURV3Y=; osd=U1kdBkrOGCwczvWsTcoo9dCc6JVWmnVba6PA_Q2BYlBwppPaJlMW0UfO_KhE1jMax-5QAwtHXS029KXl7_cQUnE=; Hm_lpvt_98beee57fd2ef70ccdd5ca52b9740c49=16\\09'\n", + "}\n", + "\n", + "start_url = 'https://www.zhihu.com/api/v4/questions/432856600/answers?include=data%5B%2A%5D.is_normal%2Cadmin_closed_comment%2Creward_info%2Cis_collapsed%2Cannotation_action%2Cannotation_detail%2Ccollapse_reason%2Cis_sticky%2Ccollapsed_by%2Csuggest_edit%2Ccomment_count%2Ccan_comment%2Ccontent%2Ceditable_content%2Cattachment%2Cvoteup_count%2Creshipment_settings%2Ccomment_permission%2Ccreated_time%2Cupdated_time%2Creview_info%2Crelevant_info%2Cquestion%2Cexcerpt%2Cis_labeled%2Cpaid_info%2Cpaid_info_content%2Crelationship.is_authorized%2Cis_author%2Cvoting%2Cis_thanked%2Cis_nothelp%2Cis_recognized%3Bdata%5B%2A%5D.mark_infos%5B%2A%5D.url%3Bdata%5B%2A%5D.author.follower_count%2Cbadge%5B%2A%5D.topics%3Bdata%5B%2A%5D.settings.table_of_content.enabled&limit=5&offset=0&platform=desktop&sort_by=default'\n", + "print(\"running.\")\n", + "\n", + "next_url = [start_url]\n", + "count = 0\n", + "time.sleep(3)\n", + "for url in next_url:\n", + " time.sleep(1)\n", + " html = requests.get(url, headers=headers)\n", + " html.encoding = html.apparent_encoding\n", + " soup = BeautifulSoup(html.text, \"lxml\")\n", + " content = str(soup.p).split(\"

\")[1].split(\"

\")[0]\n", + " c = json.loads(content)\n", + "\n", + " if \"data\" not in c:\n", + " print(\"获取数据失败，本 ip 可能已被限制。\")\n", + " print(c)\n", + " break\n", + "\n", + " answers = [extract_answer(item[\"content\"]) for item in c[\"data\"] if extract_answer(item[\"content\"]) != \"\"]\n", + " time.sleep(3)\n", + " for answer in answers:\n", + " count = count + 1\n", + " result.append(answer)\n", + " print(\"answer\", count, \":\", answer)\n", + "\n", + " next_url.append(c[\"paging\"][\"next\"])\n", + " if c[\"paging\"][\"is_end\"]:\n", + " break\n", + "\n", + "print(\"total answers:\",count)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['咱们一条一条分析。1、88-93，北京人，城六区。88-93，对应的高考就是06-11年。所以88-93，这6年，同龄人就是62.7万。城六区，占62%吧，还有38.87万人。男性占51.6%，还有20.05万人。2、身高175以上，五官端正，身材匀称。北京男性平均身高正好是175。但这不代表满大街都是175的。你可以简单理解为有一个175以上的，那势必会有一个175以下的。这个涉及加权平均数，需要各身高数值出现的次数（频数），比较难计算。概率就粗略计算为50%吧，还有10万人。五官端正，并不是大帅哥，又有点外协。那就是丑的、端正、帅哥分3类，要找其中的端正呗。那概率就算1/3，还有3.3万人。身材匀称，（北京人胖子可不少，近26%），那么不胖的就是74%，还剩2.4万人。3、一本以上学历，工资要高于女方。北京本科录取率大概50%，还剩1.2万人。哦对，光本科还不行，您还要求一本以上，那估计概率也就25%了。还剩6000人。北京平均工资9000，要超过女生1W，这个类似身高的加权平均数，那概率也50%，还剩3千人。4、忘记了一点，单身啊。最小93年，27岁了。满足以上条件的，27岁-32岁，没结婚，且没有对象的。你说还剩多少？给你往多了算算，剩一半，行吧。最后还有1500人。5、杂七杂八的你也说了，有人介绍，性格不合你朋友也不满意。把性格啊，三观啊。爱好啊，这些杂七杂八的不满意的给你淘汰了，给你淘汰1/3，还剩下多少人。1000人！北京常驻人口多少，2000多万人。您在茫茫人海，以两万分之一的概率，找这么个男人。你觉得难不难？PS:拿我自己举个例。我对照一下。我91年，180，五官端正，本科，月入2万，有车有房。不符合的地方，北京郊区的，二本，胖子。而我谈过3个女朋友都是外地的，她们全是研究生毕业，长得漂亮。我有车有房，找北京姑娘和外地姑娘有什么关系？而且，找北京姑娘人家还看不上我，又郊区又胖，哈哈哈。所以你们看不上别人的同时，别人也不见得非得找你们。你自己也说了，亲戚朋友介绍的对象，家人都觉得不是什么大问题，你还不是很满意。那你就好好单着呗。知足者常乐。接着你修改的补充一下现在我朋友受到了大家的启发，从自己的标准中选择了三个最在意的作为硬线：北京人，不胖，月薪过万。而其他的作为加分项，与人相亲时也多用加分评判别人~这个依旧没有任何意义，所谓三个最在意的作为硬线，只不过是加了个最低下限。最低要求60分，但是她依旧择优录取，所以这个最低要求有什么意义？遇到65分的，可她还是觉得下一个会更高，这有什么实质性的意义？这玩意充其量是拿来自欺欺人，掩饰自己。让别人看起来，觉得“我不挑”“我要求低，只有3点硬性要求”，哈哈哈简直自作聪明。真想找对象，设了3个硬性要求，对方符合了，那就和人家好好处，别再挑三捡四了。人无完人。一个人，恋爱前后可能判若两人，有很多问题，你不相处也发现不了，同理，你真心相处了，可能很多问题也不是问题了。最后说一句，你所谓的帮你朋友咨询，其实只不过就是在反向寻求认同。任何与你们相反的观点，都可以找各种借口和理由解释，其实没必要。掩耳盗铃，自欺欺人而已。本身事情就都是两面性，没有对错，无非看理解。只要你朋友，自己真的问心无愧，自己真的过得开心，那她说的做的一切，对她来说都是对的。当然，一个人到底真的开不开心，只有她自己知道。所以没必要在别人那里寻求认同感，自己无悔就好。',\n", + " '(눈_눈)这个妹子条件可以说想当高下面是妹子原文。我朋友北京土著女，93年，事业稳定，有时间休息，收入稳定1W+，狮子座。家里有房有车，父母有退休金和医保，养老不用愁。长相也还行，打5分吧，身高172，皮肤很白，身材匀称。27岁年龄较大而且一般女方需要男方是城6区有房。。事业稳定1w多。这个太厉害单稳定月薪1w以上基本就pass程序员这种了。我是庸人，知道有这收入的只有4种行业。警察(我认识个朋友好像是五道口职业学院毕业的)。医生。各大行工程师(我只认识一个复旦毕业的)。以及央企/国企小领导。。。。如果算地域。。北京考生能进去的。这岁数还没结婚性格还得好这种人还有多少。。其次房子问题，你想找学区房我就替你直说了吧。。后面孩子教育问题肯定必须的。一套1500w左右性格上外冷内热，有点刀子嘴豆腐心，聊天时嘴有点损，喜欢怼人，不过其实熟悉了就知道是个铁憨憨。从来没有谈过恋爱。生活上比较精致，吃穿用都是名牌。基本上衣服和护肤品用的都是四位数的。养了一条萨摩耶。妹子没必要提这种花钱爱好，家底2000w以上。事业足够稳定甚至部分是央企领导岗的男孩需要的是一个温柔可人知冷知热。气质极佳的妹子。4位数衣服。。。对于这种收入的家庭一起聚会有可能还穿不出去。她有一点外貌协会，所以见了好多男生都没啥感觉。但是陆陆续续找了三年了，亲戚朋友也介绍了好多男生给她，不过最后都因为各式各样的原因没成。有拒绝别人的，也有被别人拒绝的时候。看来妹子还特别重视男方外貌和soul。。嗯。所谓弱水三千只取一瓢。找灵魂中命定的白马王子。。。难度真的不是一般的大。。。不过还是希望妹子找到真爱吧择偶标准如下：88-93，北京人，身高175以上，五官端正，身材匀称（不要胖子），一本以上学历，最好城六区，北京市人均工资以上，要高于女方。求大家帮忙看看，我们都觉得这个标准男生应该不难达到总结下。我是庸人，按我的理解妹子的要求男方必须北京原土著。可以稳定到退休的工作(程序员这种华为阿里我认为短时间收入还不错应该可以其他的不了解)，目前月薪1-2w且有升职空间。家有至少一套学区房+任意一套城6区住房。对男方外貌要求很高，很注重打扮和着装。同时要是soulmate.能忍受妹子的各种暴脾气照顾她。懂她。',\n", + " '要求不高，但很难找，因为符合她这个要求的男人选择面太大了。北京男生可以找外地女生，毕竟外地女生在京有房有户口的也很多很多；175+男生可以找165女生，未必非得找个子高的；一本男生可以找二本女生；一万以上月收入可以找收入少甚至暂时没收入的年轻女生。很多女生都问：为何我要求不高还是找不到，因为所有女生想找的都是同一类男生。而女生的收入，消费习惯，养狗爱好，其实不是什么加分项，甚至是减分项。',\n", + " '以下只基于我个人的观点，见识有限，可能不全面：你朋友这个要求是矛盾的。我认识的，北京人基本没有太上进的，学校都是几个二本，工作上也是基于凑活的态度。原因，这个年龄，如果是北京人，应该赶上了好时候，比较有钱（拆迁发展就不说了），也有见识（当年留学浪潮北京肯定有影响），如果凑巧还挺有头脑的话（一本意味着脑子好使，即使北京也不是人人都能考上的），都出国留学发展了，一个个在国外混的都不错。男生都追求奋斗的，也就不回来了。能留下来的，都是甘于平淡的普通人，就造成这种情况了。我一北京朋友，大概二本，相亲时见的他同学，当年高中班级前列，北航毕业。最后确实没成，但当时女生本人是愿意发展的。北京的优秀本地女孩不少，如果要求本地男孩的话，学历贬值严重。至少80%是这样的，剩下的，有人可能智商好一点，有人可能奋斗心强点，但对你朋友的要求，都或多或少不满足。建议抓大放小，看清主要需求，要头脑可以找个外地清华男，要家境可以找个本地拆迁户，要事业可以找个智商在线，情商高的小老板/公务员等等。',\n", + " '不知道怎么会邀请到我。大龄女大龄男不都是这么剩下的么。。有个相亲的段子：厕所有三个位置，看第一个坑没有纸，第二个坑有屎，第三个坑没有纸又有屎，回来再看第一个坑，已经有人了。',\n", + " '这还不容易解释吗，要求太高了呗，用简单的数学计算就能搞清楚，北京有户口的人就算一千五百万吧，女人占一半好了，男人还有七百五十万，还规定了88年到93年这个年龄段，恐怕连男人总数二十分之一都达不到，就算三十二万人吧。而且岁数都一大把了，别说结婚了，大部分孩子都有了，单身没有女朋友的，离异的估计你朋友也不能考虑，那就剩下十分之一就不错，七万二千人吧，身高又冒出来了，北京的男性平均身高恐怕很难达到175，176，比你朋友高的暂且算一半吧，那就是三万六千人，北京月薪上万人很多，但是也绝到不了普遍，大部分人还是达不到的，我们姑且大胆定为三分之一的人都能过万，那么还剩下一万两千人，我们再善良一点，把五官端正放宽，剩下一万人是长得不错的。这一万人咱们再善良点，八成都住在城六区，那就是八千人，这八千人里刨掉喜欢比自己年纪大的，刨掉喜欢娇小玲珑的，刨掉喜欢二十五岁以下的，就算只刨掉一千人，也只剩下七千人了。你想吧，在茫茫人海一万多平方公里的地面上，在七千人里挑个你看的好，他还喜欢你，得多难，你还基本都不认识，要靠介绍，这七千人里还没计算富二代呀，渣男呀，赌徒呀，流氓呀等等这些可能性呢，你说难不难，真的是灰常难。最后更要命，你能肯定仅剩的小伙儿子（这岁数当小伙儿都勉强了）们就能有房吗，一个房子又得扫倒一片，最后能给你选的不过千八百个，还可能因为吃饭口味，言谈举止，消费习惯被你朋友pass掉，能满意的我认为绝不超过二千人，偌大的北京，你得有多少渠道和人脉能把这两千人推送到这位姑奶奶面前呀，缘分呀！',\n", + " '首先你朋友的一大硬伤就是没谈过恋爱，其实出去问，很少有女生会觉得自己性格不好吧？但是没恋爱过的女生真的会有些偏执，她要求的男生不是没有，而是这样的男的选择面太广了，人家土著男家庭不错的，还175，好一点本科以上的全国去北京的妹子都可以挑选，人家只要长相或者情商高一些，根本没有你朋友什么事了，所以建议就是要么提高一下自己情商，去赶紧谈谈恋爱。其次下择，可能可以找到匹配的，比如身高或者土著或者经济方面调一下，不是你朋友要求高，她的要求不过分，过分的是但凡是个单身妹子，要求都是她这个。。。。不信你可以去身边问问，全部都是身高175以上，学历工作Ok，收入高于自己，家庭无短板，性格三观好，关心老婆等等，这样的男的挑选面更广，所以女生要想高择到他们，外在都是次要的了，情商往往最重要，毕竟钱也不缺，能开心的在一起才是关键，所以由于你朋友没有恋爱经验，比较吃亏，最好的选择就是放宽一下条件了，祝好。',\n", + " '看女孩情况有这样的择偶标准不算高，但完全符合的男孩确实难碰，我认识的朋友有个跟这个标准接近，94年人家住石景山，房车都有现在自己做生意，但他老人家特别佛不想结婚，以后也不想要孩子，重点是周边女孩挺多，各种类型想找挺容易……还是靠亲戚朋友介绍吧，周边已婚这种居多，要么就是从小认识的，把从小到大的同学及同学周边挖掘一下。另外就是稍微主动点，这个条件的男孩不太愁找女朋友，一旦遇见合适的别错过。',\n", + " '要求北京土著这点可以理解，但是也不要太强求。我也帮人介绍过，确实这种会不太好找，因为北京土著真的不多，北京土著没结婚的少，北京土著没结婚还会参加相亲的更少，北京土著没结婚还会参加相亲的我还认识的更加少。',\n", + " '第一个，别人给介绍的那些男士，是否符合以上的标准，如果符合了，那为什么又不行了？第二个，自身家庭过度稳定。纯猜测。这和我身边的朋友一样，一家人其乐融融，完全是自己的生活逻辑，很幸福。但是带来的一小点问题是，这样只能要求对象来接纳这种模式，甚至有时候也不太想去改变这种状态。最后的结果就是，如果没有一个人能一开始就融入他的生活模式，那就会让他感觉很累合不来。第三个，买多贵的多精致的都没事。我猜测她也喜欢有趣的男性吧。从我身边来看，但凡家庭资产超过1亿的朋友，都不喜欢张口闭口全是品牌这些话题的，至于着装当然需要有一些套装，但日常应该用其他的单品来体现出自己的着装品味。如果不是那么有趣的但是也很富有的朋友，就会觉得花钱就能稳定住的妹子是很好的了。以上。有很多臆测，如果不准确不要带入。纯属个人一点想法。',\n", + " '时机未到！',\n", + " '因为她相亲时不止考虑底线那么简单，考虑了更多因素来知乎相亲吧相亲吧！https://www.zhihu.com/club/1254196305989505024',\n", + " '其实很多事情道理是互通的，感觉找对象跟找工作特别像。不取决于你自己什么条件，也不取决于你要求什么条件，甚至跟对方的要求也没什么太大的关系。取决于跟你竞争的人是什么条件！也就是说要么同等要求的人里，你能提供的价值最多。要么能提供同等价值的人里，你要求的最少。回到问题这个例子：你这个朋友要求对方长得帅（其他的要求先忽略）那么1.在要求对象帅的适婚女性群体里，你这个朋友能提供的价值是最多的嘛？比方说特别漂亮、特别善解人意、特别温柔、特别精通厨艺等等。或者2.在自身条件相同的适婚女性群体中，你这个朋友要求是最低的。其他人都要求“高富帅”，你这个朋友只要求“帅”，不要求“高”和“富”？所以问题出在哪了，很简单了吧！实话实说的话就是：核心竞争力不足，要求还不低！',\n", + " '哈哈，其实要求不高，也不难找，不过我建议把要求不要订那么死，差不多就行了，遇到一个眼缘的也不容易，条件过得去就好',\n", + " '作为一个三四线城市的我来看这个觉得题主上面写的要求有点高了北京人一点，就pass很多再加上后面的学历，人均工资，五官端正+身材匀称+175以上讲真一个条件满足的人很多全部都符合，那就很难讲了',\n", + " '我基本上符合吧哈哈哈哈，但是我在通州上班，超出城六区范围了但是据我所知这个条件都符合，并且能被认识的，确实太少了起码我自己的初高中同学里，符合条件的基本都结婚了，离婚的也有，离了再结的都有这个要求稍微有点不太适应大环境，本地的其实也不那么太有钱，四位数的穿用有点压力，毕竟月入1w感觉有点需要家里帮衬，当然家里就这么一个小公主也没得褒贬；外地的有钱人不一定有房有车，并且家底儿很难鉴别（因为认识一些很有钱的消费水平不高，家庭条件一般的反而大手大脚）。有钱的就不太在意女生能挣多少钱，主要看颜值身材性格了；没钱的就怕冲着房子车子不是真爱。所以想来想去，就得在标准上做出一些让步。话说回来，这种条件，追着介绍对象的估计海了去了，可能也会发现符合条件的很少吧。',\n", + " '主要还是纯北京人太少了吧？我一北京同事，也是打小胡同里长大的北京土著，一块儿长大的发小…有男有女，基本最后结婚的是北京土著的，凤毛麟角。大多都妥协了，主要还是合适的碰着的真不多。想想北京上千万人，本地土著才能有多少？多半是没谈过，所以在这方面自己的要求和期待都会多一些。被毒打过了也就能降低一些标准了。比方说，我认识的北京人，长的精神的真没几个，有的那么几个都早恋早早结婚了都。条条框框越多越不容易碰到合适的人呀适当剔除几项非必要条件吧亲朋好友介绍的相亲，质量基本上都是随着年龄逐步下滑的。（虽然也保不齐什么时候给你来个暴击，一见就特别合适，但几率太小了）这是个悲伤的事实如果实在不愿意妥协，那就单着等呗，或许缘分就在转角呢？',\n", + " '貌似不高就是难找，那看看我要求这么低，为啥也找不对象。路人级别即可，身高158-168之间都可以，家境可以低我两个档次，学历不限最好专科以上，女的，性格啥的不是绿茶即可，收入2000+。为什么找不到，原因范围小，性格问题（过于内敛）。',\n", + " '要么长得丑，又高又丑是属于特别难找对象的那一类女人。她要求的男的，北京人，工资1万以上，北京城六区内有房产，175以上，还外貌协会要求男的长得好，不能胖，这样条件的男人基本上是在整个中国普通人中的完美条件了，这样的男人找对象是可以要求女方长得好看，同时条件还不错的。而题主描述的朋友条件上是还不错的，那么3年相亲下来的男人，不是不满足条件看不上，就是满足条件被男方拒绝，这种情况下，唯一的答案就是长得丑啦，又高又丑的女孩子确实不太好找对象的。至于说女方性格上有缺陷的话，这点我觉得不可能所有相亲对象第一次见面就能发现的，所以这条不太成立，所以只能是长得丑啦。',\n", + " '哎，亲，您都邀请我两回了。。。。。。好尴尬。您这位朋友，自身很优秀，乍一看要求也不高，但是，其中那条，不要求大帅哥，就五官端正就行，看到这条我差不多就能理解了，我觉得她找不到，可能是符合其他条件的，脸或气质不喜欢，脸或气质合格了，其他条件不符合。划重点吧，我觉得您这位朋友多少有些颜控，气质控，年纪不是很小了，找的对象基本比自己还大，但那个年龄段的男生脸和气质都还好的比较少了，早就被别人挑走了，留下的也可能是有各种各样的原因耽误了，再加上其他附加条件，就不太容易找到了。建议你的朋友，要么来个姐弟恋，找小鲜肉，同时别太强求对方经济条件，要么别的条件符合，长相顺眼就试试接触接触，不能两全的情况下，有趣的灵魂还是胜过美丽的脸的。当然，您的朋友最后也许会找到全部符合条件的对象，毕竟缘分这东西如此的玄，谁也说不好什么时候遇到Mr.right～个人想法，不喜勿喷，喷也没用。',\n", + " '不要听一些人乱说，30岁左右的男生，当地人，月入1万以上，也没提男生要有单独住房，这要求一点不高，可以说是非常非常普通。这样条件的两个人结合其实在北京也就是中等偏下，未来的婚姻生活必然是俩人月光，蹭父母的房子住，孩子将来上补习班可能都需要双方老人补贴点。难点应该是外形问题，女生长相只有5分（对自身条件还过得去的男生来说，这个颜值其实是偏低了），但又是颜控，显然还处于有点少女心的状态，这就比较难了，长相不太好的女生要找长相周正的男生，这反过来了。如果是今年-明年的话，建议降低对男生颜值的要求（其实再过两三年，估计这个要求自然会降低，30岁长相一般的女生不会再有信心提这个事），提高对收入的要求，比如到2万这个区间，可能反而会容易点，可以搏一下。再过两三年的话，那就再把要求都往再降一档吧。',\n", + " '啧啧啧啧，我居然会受邀请这个问题。88-93，32-2730以后的基本都结婚，30以下的可能还在为你这个目标奋斗吧。',\n", + " '不改变就等着继续问吧',\n", + " '不知道为什么邀请我。“88-93，北京人，身高175以上，五官端正，身材匀称（不要胖子），一本以上学历，最好城六区，北京市人均工资以上，要高于女方。”我也想要这样的对象呢',\n", + " '啥条件？',\n", + " '相对你朋友条件，要求确实不能算高。但是，男生可以向下找，只要喜欢，经济差点没啥；女生倾向向上找，特别你朋友这种，经济条件不俗的同时，长相也不能差。这种男生选择面可太广了。作为富裕家庭的男生，当然首选有颜有才的，家庭条件好坏不是首要条件。打扮打扮颜值五分，外在条件一般，只能提升内在，让自己变成一个有趣的姑娘，那样才能提高概率。一个富裕姑娘，非要找条件比自己好的。这时候，她的富裕不是助力，而是阻力。女生也可以向下找啊，以你朋友的实力找个有上进心人品端正的精神小伙还是不难吧，自己啥也不缺了，又为啥非要苛求经济条件呢。找对象嘛，最重要是处着舒服！',\n", + " '是城六区的也拆出去了不过讲真跟北京您这个条件的属于不上不下的人家找个可心的外地小姑娘也挺舒服还能稍微轻上点儿不是您要求高北京爷们儿是真随遇而安您这要求的基本都是“好奔饬”那一类的我身边这类人基本本科就出国了或者研究生出国反正没在拘泥于大北京另外我不清楚具体她是哪儿忍不了但是既然找不着对眼儿的人还不如要求低点儿说不好听的“忒难伺候”咱就别再可着这要求跟这小池子里霍拢了换个大池子好好挑挑碰上了也就碰上了其实罗列了那么多也就一句话“有上进心知足有责任心门当户对”就结了就是为个共同语言和差不多的三观拽那些量化的指标儿没用',\n", + " '局限于一个圈层本来就很难。',\n", + " '符合条件的男的，不会找你朋友这样的',\n", + " '不是北京的路过',\n", + " '88-93，北京人，身高175以上，五官端正，身材匀称（不要胖子），一本以上学历，最好城六区要求绝对高身高175以上，五官端正，身材匀称（不要胖子）=秒杀90%的颜值和身材北京人一本以上学历，最好城六区=秒杀95%男生概率预期0.05*0.1=0.005一百人中没有1个概率太低实际概率可能还要乘以0.1千人挑1',\n", + " 'solo27年，如果是真的solo，那考虑一下是不是自己性魅力有问题。如果期间有性伴侣，但是基于某些原因没公开，考虑是不是要求过高。比如高富帅睡你但是不想和你确定关系。总而言之，单身27年，还是女生，还是土著看你自己表述外贸条件还行，真的很反常了',\n", + " '不知道你考虑过竞争问题吗？特别是北京本地人。在北京的剩女八十万，可是剩男只有十几万。（具体忘了）这些女生都想找个本地北京男。既然你说这个条件的北京男好找的话可以想一下竞争问题，本身北京本地男的选择就很多，现在的情况是条件还可以的北京男都在被哄抢。不好找',\n", + " '这要求高个锤子，又没要求爱情。',\n", + " '强答一番。你朋友单身的原因其实很简单，符合她要求的人少呗。我们来捋一捋她的要求：1.88-93，北京人。2.身高175以上，五官端正（指的是五官端正的普通人！并不是大帅哥朋友们！），身材匀称（不要胖子）3.一本以上学历。4.最好城六区，北京市人均工资以上，要高于女方。你以为这就完了吗？并不是，因为有一些隐形要求并没有写出来。5.没结过婚。（这一点没提过，但是基本上大家都懂。妹子连恋爱都没谈过，除非对象真的优质王老五，否则怎么会被接受？）6.不谢顶，打扮潮流（至少不土气）。从上文题主提的第二点可以看出，他大概是对于颜控有点误解。诚然，有些小帅哥真的靠着一张脸就能把地摊货穿出时装周效果，但这种人毕竟是极少数。人靠衣装马靠鞍，尤其是当选择范围指定到了从27到32岁之间。别看许多三十岁的男明星依然粉嫩，但对于普通男人而言，三十岁已经开始陆续出现中年征兆了。比如发福，比如谢顶。我猜，你朋友对于相亲过的码农们评价都不高吧？7.家里能够提供资金在北京另购房子，又或者祖传房子够大的。对不起，这是我空口白话凭空想象无中生有暗渡陈仓的污蔑之词。但，但是。我觉得，一个在北京月入过万的男生，哪怕是有北京户口，要考虑着未来房贷车贷聘礼婚礼开销未来小孩读书费用等等压力的男生，无论如何也无法做到如题主朋友一样轻松惬意地买高档消费品，又是养狗又是旅游的。那位女生看似要求工资不高。但是她的生活质量却远远不是一万工资出头的男生能供给得起的。北京户口很难得，但比起在北京买房子的难度又如何？北京本地人每一个都能轻松地在北京再买多一套房子吗？最终还不是得靠家里资金支持或者自己拼搏个世界出来。同样是月入过万，住家里的女生可以赚一万花九千块。而家里没有资金支持的本地男生，就得为自己的未来打算，月入过万都得紧巴巴地过日子。别说女生看不看得上，男生们看到女生这种花钱方法都会知难而退了。所以题主说的相亲第四点，并没有什么实际意义。赚钱多少不重要，重要的是有没有钱。（悄悄哔哔句，如果你的朋友要求真的是她提的那前四条的话，广大的码农斗士们应该都能入选。）',\n", + " '一次没恋爱过的明显不会懂怎么爱一个人和付出很多都是妈宝女恋爱完全靠想象就算条件我都符合我压根不会想去认识的',\n", + " '如果永远盯着自己身上“闪光点”，那大概率永远找不到。多看看自己有啥不好的地方，然后再找对象就容易多了。每个人的灵魂都有一个缺口，你需要做的应该是用自己的缺口去找另一个可以契合的灵魂。然后才能结合，才称完美。',\n", + " '条件不高，门当户对，问题是这样的男生太抢手了，能剩下的都是凤毛麟角',\n", + " '从条件来看，相对还是比较匹配的，年龄也不算超标。建议检查一下个人性格、为人处事习惯以及生活圈子。',\n", + " '要求门当户对很正常啊。怎么就要求高了？',\n", + " '泻药，我88的，身高191，北京土著城六区，我名有两套房，父母名下两套，都在四环边上，有辆45w+的代步车，月入税后2W9+。我觉得咱俩挺合适的。不过我去年结婚了，否则咱俩可以聊一聊。',\n", + " '你说的都是客气话，我给大家翻译翻译。首先是女方条件：我朋友北京土著女，93年，事业稳定，有时间休息，收入稳定1W+，狮子座。家里有房有车，父母有退休金和医保，养老不用愁。长相也还行，打5分吧，身高172，皮肤很白，身材匀称。性格上外冷内热，有点刀子嘴豆腐心，聊天时嘴有点损，喜欢怼人（开玩笑那种怼怼的方式~不是吵架脾气大不要误会~），不过其实熟悉了就知道是个铁憨憨。从来没有谈过恋爱。生活上比较精致，吃穿用都是名牌。基本上衣服和护肤品用的都是四位数的。养了一条萨摩耶。她有一点外貌协会，所以见了好多男生都没啥感觉。但是陆陆续续找了三年了，亲戚朋友也介绍了好多男生给她，不过最后都因为各式各样的原因没成。有拒绝别人的，也有被别人拒绝的时候。翻译如下：女，27岁，北京土著，有一份清闲工作，工资不高。跟父母住一起，长相一般，身材一般，身高较高。性格内向，母胎solo27年，直女性格。颜控，相亲3年未成功。择偶标准：88-93，北京人，身高175以上，五官端正（指的是五官端正的普通人！并不是大帅哥朋友们！），身材匀称（不要胖子），一本以上学历，最好城六区，北京市人均工资以上，要高于女方。人话：真的只有这些吗？后面你写的有符合标准的又没看上，那你的真实标准是？猜想：本地土著男性，高(175以上)，帅（五官端正的普通人？不就是帅哥吗），富（一本学历以上，人均工资以上），以上3条需同时满足，缺一不可。我的心里话：劝女方耗子尾汁，适当降低标准，市场已经给你反馈了，怎么办看你自己喽。']" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "import jieba\n", + "mytext = \" \".join(result)\n", + "mytext2 = \" \".join(jieba.cut(mytext))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "data": { + "text/plain": [ + "(-0.5, 399.5, 199.5, -0.5)" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from wordcloud import WordCloud\n", + "wordcloud = WordCloud(font_path=\"simsun.ttf\").generate(mytext2)\n", + "%pylab inline\n", + "import matplotlib.pyplot as plt\n", + "plt.imshow(wordcloud)\n", + "plt.axis(\"off\")" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "count = 0;\n", + "count2 = 0;\n", + "for i in mytext2:\n", + " if(i==\"高\"):\n", + " count = count +1;\n", + " if(i==\"低\"):\n", + " count2 = count2 +1;\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "68" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "count" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "15" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "count2" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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y/3et/4dgY+u+r7r736vqzCSXZe3u9Kwkr0nycHd/+zjOxwB3pDzWXUl+sqp2TduvPXGjwEnjY0leW1WnVdVSkhcl+dR07JNJ3jid8/Ekb4qPdbcVdxP8H939X1X160k+XFUP5gdvdmDzrs/ax7Wfy9pfyXpzd399OvbxJC/t7rur6itZuysV0m3Ek434IVX1lO5+uKoqyTuSHOrut5/ouQAWkY92Wc+vVdVtSe5I8uNZ+xUvAOtwRwoAA9yRAsAAIQWAAUIKAAOEFAAGCCkADBBSABjwP8t09odrO9KcAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig = plt.figure()\n", + "ax = fig.add_axes([0,0,1,1])\n", + "langs = ['high', 'low']\n", + "students = [68,15]\n", + "ax.bar(langs,students)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "word: ('身高', '175', '以上', '五官端正') frequency 7\n", + "word: ('身材', '匀称', '不要', '胖子') frequency 6\n", + "word: ('88', '93', '北京', '人') frequency 5\n", + "word: ('175', '以上', '五官端正', '身材') frequency 5\n", + "word: ('以上', '五官端正', '身材', '匀称') frequency 5\n", + "word: ('一本', '以上学历', '最好', '城六区') frequency 5\n", + "word: ('93', '北京', '人', '身高') frequency 4\n", + "word: ('北京', '人', '身高', '175') frequency 4\n", + "word: ('人', '身高', '175', '以上') frequency 4\n", + "word: ('五官端正', '身材', '匀称', '不要') frequency 4\n", + "word: ('匀称', '不要', '胖子', '一本') frequency 4\n", + "word: ('不要', '胖子', '一本', '以上学历') frequency 4\n", + "word: ('胖子', '一本', '以上学历', '最好') frequency 4\n", + "word: ('最好', '城六区', '北京市', '人均') frequency 4\n", + "word: ('城六区', '北京市', '人均', '工资') frequency 4\n", + "word: ('北京市', '人均', '工资', '以上') frequency 4\n", + "word: ('人均', '工资', '以上', '要') frequency 4\n", + "word: ('工资', '以上', '要', '高于') frequency 4\n", + "word: ('以上', '要', '高于', '女方') frequency 4\n", + "word: ('并', '不', '大', '帅哥') frequency 3\n", + "word: ('以上学历', '最好', '城六区', '北京市') frequency 3\n", + "word: ('北京', '土著', '没', '结婚') frequency 3\n", + "word: ('北京', '男性', '平均', '身高') frequency 2\n", + "word: ('三个', '最', '在意', '作为') frequency 2\n", + "word: ('最', '在意', '作为', '硬线') frequency 2\n" + ] + } + ], + "source": [ + "from nltk import ngrams\n", + "from nltk import FreqDist\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "tok = [\"，\",\"。\",\"、\"]\n", + "\n", + "mytext2= mytext2.replace(\"，\",\"\")\n", + "mytext2= mytext2.replace(\"。\",\"\")\n", + "mytext2= mytext2.replace(\"、\",\"\")\n", + "mytext2= mytext2.replace(\"- \",\"\")\n", + "mytext2= mytext2.replace(\"（\",\"\")\n", + "mytext2= mytext2.replace(\"）\",\"\")\n", + "\n", + "\n", + "\n", + "grams = ngrams(mytext2.split(), 4) \n", + "gramsDist = FreqDist(grams)\n", + "Genuine_common_words = [word[0] for word in gramsDist.most_common(25)] \n", + "Genuine_common_counts = [word[1] for word in gramsDist.most_common(25)] \n", + "# plt.figure(figsize=(20,5))\n", + "# gramsDist.plot(10,cumulative=False) \n", + "# plt.show()\n", + "for i,j in zip(Genuine_common_words,Genuine_common_counts): print(\"word:\", i ,\"frequency \",j)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/zhihucrawler/zhihudatastorage.png b/zhihucrawler/zhihudatastorage.png new file mode 100644 index 0000000..a8a7d72 Binary files /dev/null and b/zhihucrawler/zhihudatastorage.png differ