適合新手小白的幾個練習Python爬蟲的實戰

作者：蘿卜大雜燴 2023-01-03 08:20:15

下面我們介紹幾個非常簡單入門的爬蟲項目，相信不會再出現那種直接勸退的現象啦！

經常有新手小白在學習完 Python 的基礎知識之后，不知道該如何進一步提升編碼水平，那么此時找一些友好的網站來練習爬蟲可能是一個比較好的方法，因為高級爬蟲本身就需要掌握很多知識點，以爬蟲作為切入點，既可以掌握鞏固 Python 知識，也可能在未來學習接觸到更多其他方面的知識，比如分布式，多線程等等，何樂而不為呢！

下面我們介紹幾個非常簡單入門的爬蟲項目，相信不會再出現那種直接勸退的現象啦！

豆瓣

豆瓣作為國民級網站，在爬蟲方面也非常友好，幾乎沒有設置任何反爬措施，以此網站來練手實在是在適合不過了。

評論爬取

我們以如下地址為例子

https://movie.douban.com/subject/3878007/

可以看到這里需要進行翻頁處理，通過觀察發現，評論的URL如下：

https://movie.douban.com/subject/3878007/comments?start=0&limit=20&sort=new_score&status=P&percent_type=l

每次翻一頁，start都會增長20，由此可以寫代碼如下

def get_praise():
    praise_list = []
    for i in range(0, 2000, 20):
        url = 'https://movie.douban.com/subject/3878007/comments?start=%s&limit=20&sort=new_score&status=P&percent_type=h' % str(i)
        req = requests.get(url).text
        content = BeautifulSoup(req, "html.parser")
        check_point = content.title.string
        if check_point != r"沒有訪問權限":
            comment = content.find_all("span", attrs={"class": "short"})
            for k in comment:
                praise_list.append(k.string)
        else:
            break
    return

使用range函數，步長設置為20，同時通過title等于“沒有訪問權限”來作為翻頁的終點。

下面繼續分析評論等級。

豆瓣的評論是分為三個等級的，這里分別獲取，方便后面的繼續分析

def get_ordinary():
    ordinary_list = []
    for i in range(0, 2000, 20):
        url = 'https://movie.douban.com/subject/3878007/comments?start=%s&limit=20&sort=new_score&status=P&percent_type=m' % str(i)
        req = requests.get(url).text
        content = BeautifulSoup(req, "html.parser")
        check_point = content.title.string
        if check_point != r"沒有訪問權限":
            comment = content.find_all("span", attrs={"class": "short"})
            for k in comment:
                ordinary_list.append(k.string)
        else:
            break
    return 

def get_lowest():
    lowest_list = []
    for i in range(0, 2000, 20):
        url = 'https://movie.douban.com/subject/3878007/comments?start=%s&limit=20&sort=new_score&status=P&percent_type=l' % str(i)
        req = requests.get(url).text
        content = BeautifulSoup(req, "html.parser")
        check_point = content.title.string
        if check_point != r"沒有訪問權限":
            comment = content.find_all("span", attrs={"class": "short"})
            for k in comment:
                lowest_list.append(k.string)
        else:
            break
    return

其實可以看到，這里的三段區別主要在請求URL那里，分別對應豆瓣的好評，一般和差評。

最后把得到的數據保存到文件里。

if __name__ == "__main__":
    print("Get Praise Comment")
    praise_data = get_praise()
    print("Get Ordinary Comment")
    ordinary_data = get_ordinary()
    print("Get Lowest Comment")
    lowest_data = get_lowest()
    print("Save Praise Comment")
    praise_pd = pd.DataFrame(columns=['praise_comment'], data=praise_data)
    praise_pd.to_csv('praise.csv', encoding='utf-8')
    print("Save Ordinary Comment")
    ordinary_pd = pd.DataFrame(columns=['ordinary_comment'], data=ordinary_data)
    ordinary_pd.to_csv('ordinary.csv', encoding='utf-8')
    print("Save Lowest Comment")
    lowest_pd = pd.DataFrame(columns=['lowest_comment'], data=lowest_data)
    lowest_pd.to_csv('lowest.csv', encoding='utf-8')
    print("THE END!!!")

制作詞云

這里使用jieba來分詞，使用wordcloud庫制作詞云，還是分成三類，同時去掉了一些干擾詞，比如“一部”、“一個”、“故事”和一些其他名詞，操作都不是很難，直接上代碼。

import jieba
import pandas as pd
from wordcloud import WordCloud
import numpy as np
from PIL import Image

font = r'C:\Windows\Fonts\FZSTK.TTF'
STOPWORDS = set(map(str.strip, open('stopwords.txt').readlines()))


def wordcloud_praise():
    df = pd.read_csv('praise.csv', usecols=[1])
    df_list = df.values.tolist()
    comment_after = jieba.cut(str(df_list), cut_all=False)
    words = ' '.join(comment_after)
    img = Image.open('haiwang8.jpg')
    img_array = np.array(img)
    wc = WordCloud(width=2000, height=1800, background_color='white', font_path=font, mask=img_array, stopwords=STOPWORDS)
    wc.generate(words)
    wc.to_file('praise.png')


def wordcloud_ordinary():
    df = pd.read_csv('ordinary.csv', usecols=[1])
    df_list = df.values.tolist()
    comment_after = jieba.cut(str(df_list), cut_all=False)
    words = ' '.join(comment_after)
    img = Image.open('haiwang8.jpg')
    img_array = np.array(img)
    wc = WordCloud(width=2000, height=1800, background_color='white', font_path=font, mask=img_array, stopwords=STOPWORDS)
    wc.generate(words)
    wc.to_file('ordinary.png')


def wordcloud_lowest():
    df = pd.read_csv('lowest.csv', usecols=[1])
    df_list = df.values.tolist()
    comment_after = jieba.cut(str(df_list), cut_all=False)
    words = ' '.join(comment_after)
    img = Image.open('haiwang7.jpg')
    img_array = np.array(img)
    wc = WordCloud(width=2000, height=1800, background_color='white', font_path=font, mask=img_array, stopwords=STOPWORDS)
    wc.generate(words)
    wc.to_file('lowest.png')


if __name__ == "__main__":
    print("Save praise wordcloud")
    wordcloud_praise()
    print("Save ordinary wordcloud")
    wordcloud_ordinary()
    print("Save lowest wordcloud")
    wordcloud_lowest()
    print("THE END!!!")

海報爬取

對于海報的爬取，其實也十分類似，直接給出代碼

import requests
import json


def deal_pic(url, name):
    pic = requests.get(url)
    with open(name + '.jpg', 'wb') as f:
        f.write(pic.content)


def get_poster():
    for i in range(0, 10000, 20):
        url = 'https://movie.douban.com/j/new_search_subjects?sort=U&range=0,10&tags=電影&start=%s&genres=愛情' % i
        req = requests.get(url).text
        req_dict = json.loads(req)
        for j in req_dict['data']:
            name = j['title']
            poster_url = j['cover']
            print(name, poster_url)
            deal_pic(poster_url, name)


if __name__ == "__main__":
    get_poster()

爛番茄網站

這是一個國外的電影影評網站，也比較適合新手練習，網址如下

https://www.rottentomatoes.com/tv/game_of_thrones

我們就以權力的游戲作為爬取例子。

import requests
from bs4 import BeautifulSoup
from pyecharts.charts import Line
import pyecharts.options as opts
from wordcloud import WordCloud
import jieba


baseurl = 'https://www.rottentomatoes.com'


def get_total_season_content():
    url = 'https://www.rottentomatoes.com/tv/game_of_thrones'
    response = requests.get(url).text
    content = BeautifulSoup(response, "html.parser")
    season_list = []
    div_list = content.find_all('div', attrs={'class': 'bottom_divider media seasonItem '})
    for i in div_list:
        suburl = i.find('a')['href']
        season = i.find('a').text
        rotten = i.find('span', attrs={'class': 'meter-value'}).text
        consensus = i.find('div', attrs={'class': 'consensus'}).text.strip()
        season_list.append([season, suburl, rotten, consensus])
    return season_list


def get_season_content(url):
    # url = 'https://www.rottentomatoes.com/tv/game_of_thrones/s08#audience_reviews'
    response = requests.get(url).text
    content = BeautifulSoup(response, "html.parser")
    episode_list = []
    div_list = content.find_all('div', attrs={'class': 'bottom_divider'})
    for i in div_list:
        suburl = i.find('a')['href']
        fresh = i.find('span', attrs={'class': 'tMeterScore'}).text.strip()
        episode_list.append([suburl, fresh])
    return episode_list[:5]


mylist = [['/tv/game_of_thrones/s08/e01', '92%'],
          ['/tv/game_of_thrones/s08/e02', '88%'],
          ['/tv/game_of_thrones/s08/e03', '74%'],
          ['/tv/game_of_thrones/s08/e04', '58%'],
          ['/tv/game_of_thrones/s08/e05', '48%'],
          ['/tv/game_of_thrones/s08/e06', '49%']]


def get_episode_detail(episode):
    # episode = mylist
    e_list = []
    for i in episode:
        url = baseurl + i[0]
        # print(url)
        response = requests.get(url).text
        content = BeautifulSoup(response, "html.parser")
        critic_consensus = content.find('p', attrs={'class': 'critic_consensus superPageFontColor'}).text.strip().replace(' ', '').replace('\n', '')
        review_list_left = content.find_all('div', attrs={'class': 'quote_bubble top_critic pull-left cl '})
        review_list_right = content.find_all('div', attrs={'class': 'quote_bubble top_critic pull-right  '})
        review_list = []
        for i_left in review_list_left:
            left_review = i_left.find('div', attrs={'class': 'media-body'}).find('p').text.strip()
            review_list.append(left_review)
        for i_right in review_list_right:
            right_review = i_right.find('div', attrs={'class': 'media-body'}).find('p').text.strip()
            review_list.append(right_review)
        e_list.append([critic_consensus, review_list])
    print(e_list)


if __name__ == '__main__':
    total_season_content = get_total_season_content()

王者英雄網站

我這里選取的是如下網站

http://db.18183.com/

import requests
from bs4 import BeautifulSoup


def get_hero_url():
    print('start to get hero urls')
    url = 'http://db.18183.com/'
    url_list = []
    res = requests.get(url + 'wzry').text
    content = BeautifulSoup(res, "html.parser")
    ul = content.find('ul', attrs={'class': "mod-iconlist"})
    hero_url = ul.find_all('a')
    for i in hero_url:
        url_list.append(i['href'])
    print('finish get hero urls')
    return url_list


def get_details(url):
    print('start to get details')
    base_url = 'http://db.18183.com/'
    detail_list = []
    for i in url:
        # print(i)
        res = requests.get(base_url + i).text
        content = BeautifulSoup(res, "html.parser")
        name_box = content.find('div', attrs={'class': 'name-box'})
        name = name_box.h1.text
        hero_attr = content.find('div', attrs={'class': 'attr-list'})
        attr_star = hero_attr.find_all('span')
        survivability = attr_star[0]['class'][1].split('-')[1]
        attack_damage = attr_star[1]['class'][1].split('-')[1]
        skill_effect = attr_star[2]['class'][1].split('-')[1]
        getting_started = attr_star[3]['class'][1].split('-')[1]
        details = content.find('div', attrs={'class': 'otherinfo-datapanel'})
        # print(details)
        attrs = details.find_all('p')
        attr_list = []
        for attr in attrs:
            attr_list.append(attr.text.split('：')[1].strip())
        detail_list.append([name, survivability, attack_damage,
                            skill_effect, getting_started, attr_list])
    print('finish get details')
    return detail_list


def save_tocsv(details):
    print('start save to csv')
    with open('all_hero_init_attr_new.csv', 'w', encoding='gb18030') as f:
        f.write('英雄名字,生存能力,攻擊傷害,技能效果,上手難度,最大生命,最大法力,物理攻擊,'
                '法術攻擊,物理防御,物理減傷率,法術防御,法術減傷率,移速,物理護甲穿透,法術護甲穿透,攻速加成,暴擊幾率,'
                '暴擊效果,物理吸血,法術吸血,冷卻縮減,攻擊范圍,韌性,生命回復,法力回復\n')
        for i in details:
            try:
                rowcsv = '{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}'.format(
                    i[0], i[1], i[2], i[3], i[4], i[5][0], i[5][1], i[5][2], i[5][3], i[5][4], i[5][5],
                    i[5][6], i[5][7], i[5][8], i[5][9], i[5][10], i[5][11], i[5][12], i[5][13], i[5][14], i[5][15],
                    i[5][16], i[5][17], i[5][18], i[5][19], i[5][20]
                )
                f.write(rowcsv)
                f.write('\n')
            except:
                continue
    print('finish save to csv')


if __name__ == "__main__":
    get_hero_url()
    hero_url = get_hero_url()
    details = get_details(hero_url)
    save_tocsv(details)

好了，今天先分享這三個網站，咱們后面再慢慢分享更多好的練手網站與實戰代碼！

責任編輯：武曉燕來源：蘿卜大雜燴

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