ticket? jobs? Object? Python elegant teach you to solve three major problems of the year!

Author | data does not brag

Recently, many of my friends have been many days immersed in the forthcoming (have) a holiday pleasure, incarnation fisherman, dedicated to fishing in troubled waters.

but! You really ready New Year Well! Do you really know why the New Year is called the year is it?

So that small Z to give you a story, Once there was a beast, a spring festival on ....

Sorry! Wrong studio, and all 2020, and the New Year was called the year, not because young people of East and West drift home for the holiday, there are always three is not open around the problem:

The right to buy tickets yet?
Your professional / work in the end is Gansha? How much pay?
You object?

The right ticket, by single years of hand speed, rush tickets indomitable perseverance, and that the situation in the centuries do not speak of friends help link a mass insight into the situation in the run, grab a large probability is no problem.

To solve the ticket, the mind has emerged, with a smile looking back two screens relatives face problems.

Fortunately, we all know from childhood, never failing think saying!

"Standing on the shoulders of our predecessors point of view the problem, on the height, I have a large portion of children can be higher than his" famous words (small Z nonsense), it gave me endless inspiration.

Speaking shoulders of our predecessors, know almost not that a great frame of reference it? So, we know almost Python crawling up on Hot-related issues:

Take a look at how many people have similar confusion, more importantly, what can be used to ready to use cheats.

Almost crawling known portion following code, attached to the end of the complete code and text data, skipping does not affect the reading.

import pandas as pd
import numpy as np
import os
import json
import requests

def parse_page(url,headers):
    html  = requests.get(url,headers = headers)
    bs = json.loads(html.text)
    result = pd.DataFrame()
    for i in bs['data']:
        headline = i['author']['headline'] #签名
        gender = i['author']['gender']  #性别
        user_type =  i['author']['user_type']
        user_id =  i['author']['id']
        user_token = i['author']['url_token']
        follwer_count = i['author']['follower_count'] #关注人数
        name = i['author']['name']   #用户昵称
        vote_up = i['voteup_count']  #点赞数
        updated_time = i['updated_time']    #更新时间
        title = i['question']['title']   #问题
        created_time = i['created_time'] #创建时间
        comment_count = i['comment_count'] #评论数
        can_comment = i['can_comment']['status']   #是否可以评论
        content = i['content']  #内容，还需要再清洗
        cache = pd.DataFrame({'用户ID':[user_id],'用户名':[name],'性别':[gender],'token':[user_token],'用户类型':[user_type],'签名':[headline],
                              '被关注人数':[follwer_count],'创建时间':[created_time],'更新时间':[updated_time],'评论数':[comment_count],
                              '点赞数':[vote_up],'是否可以评论':[can_comment],'内容':[content],'问题':[title]})
        result = pd.concat([result,cache])
    return result

A rainy day, or afterwards Tucao?

We found that "New Year back home, relatives ask is doing the work, to say the truth?" This issue, most of them with their own experiences to Tucao.

Should be the phrase "Tucao not avoid pro" the old saying, 91.68 percent of users do not have anonymous, of course, may not be aware of relatives Sounds almost.

Unknown Gender user too much, the proportion of men and women from the only point of view, it seems that the will of the male Tucao stronger.

Then, even more interesting answer came time distribution:

February 2018 No. (small years ago one day) 7, the main problem proactive know almost thrown at the problem, there are 15 enthusiastic people initiate the same day, in a small (No. 8) peaked add 68 days to answer .

However, after the 9th year is getting closer, answer a few less and less, after the February 15 New Year's Eve, New respondents disappeared, does everybody ready? But this is more likely to know almost traffic and distribution related.

问题沉寂了一年之后，热度在19年2月9日（大年初五）一飞冲天，221个热乎的回答蹭蹭涌入，事后吐槽之密集，以身手敏捷著称的七大姑八大姨都没能躲过。

但是，初五的辉煌，初六没能承接上，初七之后更是回归平淡。这个回答，今年还会不会再蹦跶呢？