微博爬虫及舆情分析-1.爬取微博数据_爬取的微博数据怎么存到csv

本文以“Mate30”为关键字进行分析

import requests as re
import time
#搜索关键字Mate30并获取url
target_url = "https://m.weibo.cn/api/container/getIndex?containerid=100103type%3D1%26q%3DMate30&page_type=searchall&page="
# 爬取的微博总页数
total_page=400
#存放微博与用户数据
mblog = list()
user = list()
# 根据是否为长文本采集不同的微博文本数据
long_text_count = 0
for page in range(total_page):
    print('Crawling page:%d/%d'%(page+1,total_page))
    cur_url=target_url + str(page+1)
    source_json = re.get(cur_url).json()
    time.sleep(1)
    source_data=source_json['data']
    cards = source_data['cards'] if 'cards' in source_data else []
    for card in cards:
        if 'mblog' in card:
            cur_blog = card['mblog']
            cur_text = ''
            is_long_text = 0
            if cur_blog['isLongText'] == True:
                cur_text = cur_blog['longText']['longTextContent']
                is_long_text = 1
            else:
                cur_text = cur_blog['text']
            verified_reason = cur_blog['user']['verified_reason'] if 'verified_reason' in cur_blog['user'] else None 
            # 微博数据记录
            mblog.append({'mid':cur_blog['mid']
                         ,'raw_text':cur_text
                         ,'isLongText':is_long_text
                         ,'like_count':cur_blog['attitudes_count']
                         ,'comments_count':cur_blog['comments_count']
                         ,'reposts_count':cur_blog['reposts_count']
                         ,'created_at':cur_blog['created_at']
                         ,'source':cur_blog['source']
                         ,'user_id':cur_blog['user']['id']
                         })
            # 用户数据记录
            user.append({'user_id':cur_blog['user']['id']
                        ,'screen_name':cur_blog['user']['screen_name']
                        ,'description':cur_blog['user']['description']
                        ,'follow_count':cur_blog['user']['follow_count']
                        ,'followers_count':cur_blog['user']['followers_count']
                        ,'gender':cur_blog['user']['gender']
                        ,'verified':cur_blog['user']['verified']
                        ,'verified_reason':verified_reason
                        ,'profile_url':cur_blog['user']['profile_url']
                        })
print('Crawl finished')
print('共爬取数据条数：',len(mblog))
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将微博数据转换为pandas数据，便于分析与保存

import pandas as pd
mblog_frame = pd.DataFrame(mblog)
user_frame = pd.DataFrame(user)
# 去除重复数据
mblog_frame.drop_duplicates(subset=['mid'],inplace=True)
user_frame.drop_duplicates(subset=['user_id'],inplace=True)
#数据保存到文件
mblog_frame.to_csv('mblog.csv',index=None,encoding='utf_8_sig')
user_frame.to_csv('users.csv', encoding='utf_8_sig',index=False)
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获取用户地理位置信息（由于在微博网站在用户信息部分没有地理信息，这个部分花费了很多时间，最终选择跳转到用户主页来爬取用户的地理信息）

def get_containerid(id):
    """
    获取用户的containerid
    """
    containerid_url = 'https://m.weibo.cn/api/container/getIndex?type=uid&value='+id
    containerid_json = re.get(containerid_url).json()
    containerid_data = containerid_json['data'] if 'data' in containerid_json else None
    if not containerid_data:
        return None
    tabsInfo = containerid_data['tabsInfo'] if 'tabsInfo'in containerid_data else None  
    if not tabsInfo:
        return None
    tabs = tabsInfo['tabs'] if 'tabs' in tabsInfo else None
    if not tabs:
        return None
    containerid = tabs[0]['containerid'] if 'containerid'in tabs[0] else None
    return containerid

def get_user_location(raw):
    profile_url = raw['profile_url']
    left,right = profile_url.split('?')
    uid = left.split('/')[-1]
    pre = 'https://m.weibo.cn/api/container/getIndex?'
    mid = '&type=uid&value='
    containerid = get_containerid(uid)
    if not containerid:
        return None
    final_url = pre+right+mid+uid+'&containerid='+containerid
    local_json = re.get(final_url).json()
    data = local_json['data'] if 'data' in local_json else None
    if not data:
        return None
    cards = data['cards'] if 'cards' in data else None
    if not cards:
        return None
    card_group = cards[0]['card_group'] if 'card_group' in cards[0] else None
    if not card_group:
        return None
    location = card_group[0]['item_content'] if 'item_content' in card_group[0] else None
    return location
#批量处理获取用户所在区域并将结果更新保存到文件
user_frame['location'] = user_frame.apply(get_user_location,axis=1)
user_frame.to_csv('users.csv',index=None,encoding='utf_8_sig')
#若要保存到数据库，运行下面的代码
import pymysql
from sqlalchemy import create_engine
engine = create_engine('mysql+pymysql://root:password@localhost:3306/weibo_keyword?charset=utf8mb4',echo=False)
mblog_frame.to_sql(name='ori_mblog',con=engine,if_exists='append',index=False)
user_frame.to_sql(name='ori_users',con=engine,if_exists='append',index=False)
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