<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[m0_50833892的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/m0_50833892</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; m0_50833892]]></copyright><item><title><![CDATA[Python爬虫-爬取国外新闻网站标题和文本信息]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/144944324</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/144944324</guid><author>m0_50833892</author><pubDate>Sun, 05 Jan 2025 13:29:52 +0800</pubDate><description><![CDATA[种子 URL：爬虫从一个或多个种子 URL 开始，这些 URL 是起点。发送请求：爬虫向这些种子 URL 发送 HTTP 请求，通常是 GET 请求。获取响应：服务器返回网页的 HTML 内容作为响应。解析内容：爬虫解析 HTML 内容，提取所需的数据（如文本、链接、图片等）。提取链接：从网页中提取出所有链接，并将这些链接加入待访问队列。重复过程：爬虫重复上述步骤，直到达到某个停止条件，如爬取了一定数量的页面，或所有页面都被爬取完毕。爬虫的分类。]]></description><category></category></item><item><title><![CDATA[面板数据标准化（熵权法）标准化+熵值+权重]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/144669663</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/144669663</guid><author>m0_50833892</author><pubDate>Mon, 23 Dec 2024 15:56:44 +0800</pubDate><description><![CDATA[面板数据标准化（熵权法）标准化+熵值+权重

import numpy as np
import pandas as pd
from openpyxl import load_workbook

读取数据
data = pd.read_excel('./XX.xlsx', sheet_name="XX")

需要标准化的列
columns_to_standardize = ['y', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8']

定义标准化函数，将数据标准]]></description><category></category></item><item><title><![CDATA[基于Python的障碍度模型分析和熵权法-障碍度技术路线讲解]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/144412848</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/144412848</guid><author>m0_50833892</author><pubDate>Wed, 11 Dec 2024 23:56:09 +0800</pubDate><description><![CDATA[可能存在障碍度为 0 的情况，当对象的某个指标是所有对象中最高的，极差法之后结果是 1，偏离度为 0，最后的障碍度也为 0，即该指标已经满分，没有障碍，无需优化。障碍度模型一般配合熵权topsis使用，当基于topsis给对象排序之后，可以继续用障碍度模型计算每个对象的待优化项，障碍度越高越亟需优化。计算单一指标对总目标的贡献，一般用指标的全局权重表示，记作 ( F )。根据指标层级关系，将底层指标的障碍度向上求和汇总，得到上层指标的障碍度。计算指标偏离度 ( I )，表示指标实际值与最优值之间的差距。]]></description><category></category></item><item><title><![CDATA[基于豆瓣爬虫的pylab数据动态交互显示]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/121652595</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/121652595</guid><author>m0_50833892</author><pubDate>Wed, 01 Dec 2021 13:03:58 +0800</pubDate><description><![CDATA[爬虫+ pylab数据动态交互显示

云计算课上的小实训，用一个上午来写  崩的

文章目录爬虫+ pylab数据动态交互显示前言一、爬取豆瓣的网站获取想要的contents二、使用步骤1.引入库数据连接pymatlabpost一个完整的代码4.效果展示总结


前言
老师想让我们做一个基于爬虫的数据可视化，要实时，要交互！！

提示：以下是本篇文章正文内容，下面案例可供参考
一、爬取豆瓣的网站获取想要的contents
headers={'User-Agent': 'Mozilla/5.0 (Window]]></description><category></category></item><item><title><![CDATA[输出前 n 个Fibonacci数]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/118496838</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/118496838</guid><author>m0_50833892</author><pubDate>Mon, 05 Jul 2021 19:38:36 +0800</pubDate><description><![CDATA[前言
输出前 n 个Fibonacci数
提示：这里可以添加本文要记录的大概内容：
本题要求编写程序，输出菲波那契（Fibonacci）数列的前N项，每行输出5个，题目保证输出结果在长整型范围内。Fibonacci数列就是满足任一项数字是前两项的和（最开始两项均定义为1）的数列，例如：1，1，2，3，5，8，13，…。

提示：以下是本篇文章正文内容，下面案例可供参考
输入格式:
输入在一行中给出一个整数N（1≤N≤46）。
输出格式:
输出前N个Fibonacci数，每个数占11位，每行输出5个。如果最后]]></description><category></category></item><item><title><![CDATA[多裁判制，转盘，抓兔子等期末程序设计编程题]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/112681631</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/112681631</guid><author>m0_50833892</author><pubDate>Fri, 15 Jan 2021 18:19:29 +0800</pubDate><description><![CDATA[多裁判制，转盘，抓兔子等期末程序设计编程题第一个抓兔子~~第二个，转盘小游戏先说一下游戏规则吧，第三个游戏 我把它叫做多裁判评分制游戏，(可能是我对游戏的理解比较强烈哈哈哈)游戏比较简单，可以说这次的期末考试叶不难，但是我为什么只有六十分？？？？ 难道是老师对我的编程不满意？？？？
第一个抓兔子~~
import random
try:
    o = int(input("想要定义的洞口数："))
    p = int(input("玩家最多参与游戏的次数："))
except:
    print("]]></description><category></category></item><item><title><![CDATA[豆瓣读书top250爬取]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/112440701</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/112440701</guid><author>m0_50833892</author><pubDate>Sun, 10 Jan 2021 20:21:41 +0800</pubDate><description><![CDATA[练习豆瓣爬取读书top 250 13：12   非	json 格式

from urllib.request import urlopen,Request
from bs4 import BeautifulSoup
import time
import xlwt
headers={'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.121 Safa]]></description><category></category></item><item><title><![CDATA[爬取世界五百强]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/112393535</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/112393535</guid><author>m0_50833892</author><pubDate>Sat, 09 Jan 2021 16:27:31 +0800</pubDate><description><![CDATA[from urllib.request import urlopen, Request
from bs4 import BeautifulSoup
from time import sleep
headers = {
    'User-Agent': ' Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36 '}
html = u]]></description><category></category></item><item><title><![CDATA[爬取豆瓣电影.json]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/112393473</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/112393473</guid><author>m0_50833892</author><pubDate>Sat, 09 Jan 2021 16:24:27 +0800</pubDate><description><![CDATA[目前只抓了个html下来。。。
from urllib import request
import json


headers={'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.88 Safari/537.36'}
def fetch_data(url):
    req = request.Request(url,headers=]]></description><category></category></item><item><title><![CDATA[爬取豆瓣音乐top250]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/112393415</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/112393415</guid><author>m0_50833892</author><pubDate>Sat, 09 Jan 2021 16:21:31 +0800</pubDate><description><![CDATA[爬取豆瓣音乐并写入excel
from urllib.request import urlopen,Request
from bs4 import BeautifulSoup
import time
import xlwt
titles=[]
time_list=[]
attribute_list=[]
zhuanji_list=[]
score=[]
headers={'User-Agent':'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:6.0) Gecko/20100]]></description><category></category></item><item><title><![CDATA[论文的下载]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/112393361</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/112393361</guid><author>m0_50833892</author><pubDate>Sat, 09 Jan 2021 16:18:53 +0800</pubDate><description><![CDATA[论文下载写入文件夹
from urllib.request import urlopen,Request
from bs4 import BeautifulSoup
from urllib.request import urlretrieve
import os
headers={'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3947.100 Safa]]></description><category></category></item><item><title><![CDATA[前程无忧网站爬取]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/112393343</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/112393343</guid><author>m0_50833892</author><pubDate>Sat, 09 Jan 2021 16:17:41 +0800</pubDate><description><![CDATA[爬取前程无忧网站 写入excel
from urllib.request import urlopen,Request
from bs4 import BeautifulSoup
import json
import xlwt
lentitles=[]
job_href=[]
company_name=[]
providesalary_text=[]
workarea_text=[]
jobwelf=[]
companyind_text=[]
careerinfo=[]

workbook=xlwt.Wor]]></description><category></category></item><item><title><![CDATA[列维坦油画爬取]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/112393318</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/112393318</guid><author>m0_50833892</author><pubDate>Sat, 09 Jan 2021 16:16:36 +0800</pubDate><description><![CDATA[from urllib.request import urlopen,Request
from bs4 import BeautifulSoup
import os
from urllib.request import urlretrieve

headers={'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3947.100 Safari/537.36]]></description><category></category></item><item><title><![CDATA[json 爬取图片保存本地3.0]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/112391526</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/112391526</guid><author>m0_50833892</author><pubDate>Sat, 09 Jan 2021 14:36:42 +0800</pubDate><description><![CDATA[from urllib import request
import json
import os
from urllib.request import urlretrieve

def fetch_data(url):
    req = request.Request(url)  # 请求url（GET请求）
    with request.urlopen(req) as f:     # 打开url请求（如同打开本地文件一样）
        return json.loads(f.read().de]]></description><category></category></item><item><title><![CDATA[乱世佳人小说网页的爬取]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/109559660</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/109559660</guid><author>m0_50833892</author><pubDate>Sun, 08 Nov 2020 15:20:35 +0800</pubDate><description><![CDATA[乱世佳人的小说文本爬取
from urllib.request import urlopen, Request
from bs4 import BeautifulSoup
titles=[]
texts=[]
首先引用urlopen ，Request，Beautiful
建立两个列表存放标题和每篇文章的内容
def updatepages(url, header):
    ret = Request(url, headers=header) 
    html = urlopen(ret).read().]]></description><category></category></item><item><title><![CDATA[2020-10-26]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/109287773</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/109287773</guid><author>m0_50833892</author><pubDate>Mon, 26 Oct 2020 13:09:18 +0800</pubDate><description><![CDATA[from tkinter import *
import random
import pygame as py
file=r’D:\CloudMusic\ttdd.mp3’
py.mixer.init()
py.mixer.music.load(file)
py.mixer.music.play(-1, 30)
“”“音乐播放 -1代表循环播放， 第二个参数代表开始播放的时间(秒”""
going = True
ungoing= False
data = [‘周嘉铖’,‘钱珑超’,‘徐展’,‘尤桉哲’,‘钱]]></description><category></category></item><item><title><![CDATA[小游戏]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/108611082</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/108611082</guid><author>m0_50833892</author><pubDate>Tue, 15 Sep 2020 22:46:09 +0800</pubDate><description><![CDATA[第二章 设计一个的报数小游戏。
28人的小游戏 ，从1到28 号报数重复1-3，报到3的人坐下，到最后剩下俩人的序号。
list=([i for i in range(1,29)])
list 
[1,
 2,
 3,
 4,
 5,
 6,
 7,
 8,
 9,
 10,
 11,
 12,
 13,
 14,
 15,
 16,
 17,
 18,
 19,
 20,
 21,
 22,
 23,
 24,
 25,
 26,
 27,
 28]

将前两个数字设为变量a,b
用list.pop()将]]></description><category></category></item><item><title><![CDATA[markdown的学习]]></title><link>https://blog.youkuaiyun.com/m0_50833892/article/details/108601757</link><guid>https://blog.youkuaiyun.com/m0_50833892/article/details/108601757</guid><author>m0_50833892</author><pubDate>Tue, 15 Sep 2020 18:52:06 +0800</pubDate><description><![CDATA[第一章 从学习markdown开始！！！！！首先我们来学习一下代码的展示代码片尝试改变文本
首先我们来学习一下代码的展示
代码片
print("hello world")

print( for i in range(1,29)：
while ...
尝试改变文本
删除

强调 * 强调*
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co2是二氧化碳



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