1905电影网中国地区电影数据分析（一） - 数据采集、清洗与存储

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前言

本项目旨在通过爬取1905电影网的电影数据，展示如何使用Python及相关库进行网页数据采集。本项目将详细介绍数据采集的步骤，包括所需的Python库版本、网页分析、数据提取和保存等环节。我们将使用requests库进行网络请求，利用BeautifulSoup进行HTML解析，并将最终的数据保存为CSV文件，便于后续分析和处理。

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一、数据采集步骤及python库使用版本

1. python库使用版本

	python	requests	bs4	beautifulsoup4	soupsieve	lxml	pandas	sqlalchemy	mysql-connector-python	selenium
版本	3.8.5	2.31.0	0.0.2	4.12.3	2.6	4.9.3	2.0.3	2.0.36	9.0.0	4.15.2

2. 数据采集步骤

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二、数据采集网页分析

1. 分析采集的字段和URL

1.1 分析要爬取的数据字段

如下图所示，红框部分是要爬取的数据，包含电影标题、电影类型、电影时长、电影片名、电影别名、电影上映时间、电影编剧、电影导演、电影主演、电影剧情等字段。

1.2 分析每部电影的URL

访问中国地区的电影地址：https://www.1905.com/mdb/film/list/country-China/

如下图所示，电影是分页显示，每一页有多部电影，点击单部电影后会调转到对应主页，在对应的主页就有需要爬取的数据，所以需要从每页中解析出单个电影的URL。

如下图所示，检查单部电影的源码后可以看到对应的URL。
复制该部电影的URL为：https://www.1905.com/mdb/film/2248201/（2248201是这部电影的ID）
那么就可以通过解析网页获取到每部电影的URL。

1.2 分析每页的URL

如下图所示，检查源码后发现如下规律：
第二页的URL为：https://www.1905.com/mdb/film/list/country-China/o0d0p2.html
第三页的URL为：https://www.1905.com/mdb/film/list/country-China/o0d0p3.html
第四页的URL为：https://www.1905.com/mdb/film/list/country-China/o0d0p4.html
第五页的URL为：https://www.1905.com/mdb/film/list/country-China/o0d0p5.html

由此推断出：
第一页的URL为：https://www.1905.com/mdb/film/list/country-China/o0d0p1.html（o0d0p1.html可省略）
第n页的URL为：https://www.1905.com/mdb/film/list/country-China/o0d0p{n}.html

2. 字段元素标签定位

示例：定位电影标题元素
定位后的CSS选择器内容为：

body > div.topModule.normalCommon.normal_oneLine > div > div > div.topModule_title.clearfix > div.topModule_title_left.fl > h3 > span

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三、数据采集代码实现

1. 爬取1905电影网分类信息

import random
import time
from pathlib import Pathimport pandas as pd
import requests
from bs4 import BeautifulSoup'''
爬取1905电影网分类信息（大分类 main_category，小分类 sub_category，链接 sub_category_link）
'''def get_request(url, **kwargs):time.sleep(random.uniform(0.1, 2))print(f'===============================请求地址：{url} ===============================')# 定义一组User-Agent字符串user_agents = [# Chrome'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36','Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36',# Firefox'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/117.0','Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:109.0) Gecko/20100101 Firefox/117.0','Mozilla/5.0 (X11; Linux i686; rv:109.0) Gecko/20100101 Firefox/117.0',# Edge'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36 Edg/117.0.2040.0',# Safari'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.5 Safari/605.1.15',]# 请求头headers = {'User-Agent': random.choice(user_agents)}# 用户名密码认证(私密代理/独享代理)username = ""password = ""proxies = {"http": "http://%(user)s:%(pwd)s@%(proxy)s/" % {"user": username, "pwd": password,"proxy": '36.25.243.5:11768'},"https": "http://%(user)s:%(pwd)s@%(proxy)s/" % {"user": username, "pwd": password,"proxy": '36.25.243.5:11768'}}max_retries = 3for attempt in range(max_retries):try:response = requests.get(url=url, timeout=10, headers=headers, **kwargs)# response = requests.get(url=url, timeout=10, headers=headers, proxies=proxies, **kwargs)if response.status_code == 200:return responseelse:print(f"请求失败，状态码: {response.status_code}，正在重新发送请求 (尝试 {attempt + 1}/{max_retries})")except requests.exceptions.RequestException as e:print(f"请求过程中发生异常: {e}，正在重新发送请求 (尝试 {attempt + 1}/{max_retries})")# 如果不是最后一次尝试，则等待一段时间再重试if attempt < max_retries - 1:time.sleep(random.uniform(1, 2))print('================多次请求失败，请查看异常情况================')return None  # 或者返回最后一次的响应，取决于你的需求def get_soup(markup):return BeautifulSoup(markup=markup, features='lxml')def save_categories_to_csv(response, csv_file_dir='./data_csv/', csv_file_name='category.csv'):"""从HTML响应中提取分类信息并保存到CSV文件。参数:response (requests.Response): 包含HTML内容的响应对象。csv_file_dir (str): CSV文件存储目录，默认为'./data_csv/'。csv_file_name (str): CSV文件名，默认为'category.csv'。"""# 确保目录存在csv_file_dir_path = Path(csv_file_dir)csv_file_dir_path.mkdir(parents=True, exist_ok=True)# 解析HTML文档soup = get_soup(response.text)# 提取分类信息data_list = []tag_srh_group = soup.select("body > div.layout.mainCont.clear > div.leftArea > div > div.col-l-bd > dl.srhGroup.clear")for tag_srh in tag_srh_group:tag_dt = tag_srh.select_one('dt')main_category = tag_dt.text.strip() if tag_dt is not None else Nonetag_a_list = tag_srh.select('a')print(f'===========================解析后的数据如下：===========================')for tag_a in tag_a_list:if tag_a is not None:sub_category = tag_a.text.strip()sub_category_link = 'https://www.1905.com' + tag_a.get('href', '')data_dict = {'main_category': main_category,'sub_category': sub_category,'sub_category_link': sub_category_link}data_list.append(data_dict)print(data_dict)# 创建DataFrame并清理数据df = pd.DataFrame(data_list)df_cleaned = df[df['sub_category'].notna() & (df['sub_category'] != '')]print(f'===========================文件保存路径：{csv_file_dir + csv_file_name}===========================')# 保存到CSV文件df_cleaned.to_csv(csv_file_dir + csv_file_name, index=False, encoding='utf-8-sig')if __name__ == '__main__':res = get_request("https://www.1905.com/mdb/film/search/")save_categories_to_csv(res)

保存后的文件内容如下图所示：

2. 爬取电影主页HTML

import random
import time
from pathlib import Pathimport requests
from bs4 import BeautifulSoupdef get_request(url, **kwargs):time.sleep(random.uniform(0.1, 2))print(f'===============================请求地址：{url} ===============================')# 定义一组User-Agent字符串user_agents = [# Chrome'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36','Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36',# Firefox'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/117.0','Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:109.0) Gecko/20100101 Firefox/117.0','Mozilla/5.0 (X11; Linux i686; rv:109.0) Gecko/20100101 Firefox/117.0',# Edge'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36 Edg/117.0.2040.0',# Safari'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.5 Safari/605.1.15',]# 请求头headers = {'User-Agent': random.choice(user_agents)}# 用户名密码认证(私密代理/独享代理)username = ""password = ""proxies = {"http": "http://%(user)s:%(pwd)s@%(proxy)s/" % {"user": username, "pwd": password,"proxy": '36.25.243.5:11768'},"https": "http://%(user)s:%(pwd)s@%(proxy)s/" % {"user": username, "pwd": password,"proxy": '36.25.243.5:11768'}}max_retries = 3for attempt in range(max_retries):try:response = requests.get(url=url, timeout=10, headers=headers, **kwargs)# response = requests.get(url=url, timeout=10, headers=headers, proxies=proxies, **kwargs)if response.status_code == 200:return responseelse:print(f"请求失败，状态码: {response.status_code}，正在重新发送请求 (尝试 {attempt + 1}/{max_retries})")except requests.exceptions.RequestException as e:print(f"请求过程中发生异常: {e}，正在重新发送请求 (尝试 {attempt + 1}/{max_retries})")# 如果不是最后一次尝试，则等待一段时间再重试if attempt < max_retries - 1:time.sleep(random.uniform(1, 2))print('================多次请求失败，请查看异常情况================')return None  # 或者返回最后一次的响应，取决于你的需求def get_soup(markup):return BeautifulSoup(markup=markup, features='lxml')def save_html_file(save_dir, file_name, content):dir_path = Path(save_dir)# 确保保存目录存在，如果不存在则创建所有必要的父级目录dir_path.mkdir(parents=True, exist_ok=True)# 使用 'with' 语句打开文件以确保正确关闭文件流with open(save_dir + file_name, 'w', encoding='utf-8') as fp:print(f"==============================={save_dir + file_name} 文件已保存===============================")fp.write(str(content))def save_rough_html_file():i = 0save_dir = './rough_html/china/'while True:i = i + 1file_name = f'o0d0p{i}.html'file_path = Path(save_dir + file_name)if file_path.exists() and file_path.is_file():print(f'===============================文件 {file_path} 已存在===============================')continueurl = f'https://www.1905.com/mdb/film/list/country-China/o0d0p{i}.html'response = get_request(url)soup = get_soup(response.text)tag_ul = soup.select_one('body > div.layout.mainCont.clear > div.leftArea > ul')if tag_ul.text.strip() is None or tag_ul.text.strip() == '':print(f'===============================网页爬取完成===============================')breaksave_html_file(save_dir, file_name, response.text)def save_detail_info_html_file():i = 0save_dir = './detail_html/china/'while True:i = i + 1url = f'https://www.1905.com/mdb/film/list/country-China/o0d0p{i}.html'response = get_request(url)soup = get_soup(response.text)tag_ul = soup.select_one('body > div.layout.mainCont.clear > div.leftArea > ul')if tag_ul.text.strip() is None or tag_ul.text.strip() == '':print(f'===============================网页爬取完成===============================')breaktag_li_list = tag_ul.select('li')for tag_li in tag_li_list:tag_a_href = tag_li.find('a').attrs.get('href')movie_url = f'https://www.1905.com{tag_a_href}'movie_id = tag_a_href.split('/')[-2]file_name = f'{movie_id}.html'file_path = Path(save_dir + file_name)if file_path.exists() and file_path.is_file():print(f'===============================文件 {file_path} 已存在===============================')continuedetail_response = get_request(movie_url)if detail_response is None:continuesave_html_file(save_dir, file_name, detail_response.text)if __name__ == '__main__':# save_rough_html_file()save_detail_info_html_file()

爬取后保存的部分html文件如下图所示：

3. 解析html并把数据保存到csv文件

from pathlib import Pathimport pandas as pd
from bs4 import BeautifulSoupdef get_soup(markup):return BeautifulSoup(markup=markup, features='lxml')def parse_detail_html_to_csv():# 定义CSV文件路径csv_file_dir = '../1905movie/data_csv/'csv_file_name = 'detail_1905movie_dataset.csv'csv_file_path = Path(csv_file_dir + csv_file_name)csv_file_dir_path = Path(csv_file_dir)csv_file_dir_path.mkdir(parents=True, exist_ok=True)detail_dir = Path('./detail_html/china/')detail_file_list = detail_dir.rglob('*.html')movie_data_list = []i = 0count = 0for detail_file in detail_file_list:movie_id = str(detail_file).split('\\')[-1].split('.')[0]movie_url = f'https://www.1905.com/mdb/film/{movie_id}/'soup = get_soup(open(file=detail_file, mode='r', encoding='utf-8'))tag_img_url = soup.select_one('div.topModule_bottom_poster.picHover.fl img')movie_img_url = tag_img_url.attrs.get('src') if tag_img_url is not None else Nonetag_div_topmodule_title_right = soup.select_one('div.topModule_title_right.fr')tag_evaluation_name = tag_div_topmodule_title_right.select_one('div.evaluation-name')tag_judge_soon_fl = tag_div_topmodule_title_right.select_one('div.judge-soon.fl')movie_rating = tag_evaluation_name.text if tag_evaluation_name is not None else Nonemovie_status = tag_judge_soon_fl.text if tag_judge_soon_fl is not None else '已上映'tag_topmodule_title_left_fl = soup.select_one('div.topModule_title_left.fl')tag_h3_span = tag_topmodule_title_left_fl.select_one('h3 > span')movie_title = tag_h3_span.text if tag_h3_span is not None else Nonetag_li = tag_topmodule_title_left_fl.select_one('li.topModule_line')movie_genres = str(tag_li.find_next_sibling('li').text.strip()).split() if tag_li is not None else Nonetag_li5 = tag_topmodule_title_left_fl.select_one('div > ul > li:nth-child(5)')movie_duration = tag_li5.text.strip() if tag_li5 is not None else Nonetag_div_left_top = soup.select_one('div#left_top')tag_ul_consmodule_infos = tag_div_left_top.select_one('ul.consModule_infos.consModule_infos_l.fixedWidth.fl') if tag_div_left_top is not None else Nonetag_li_em_release_date = tag_ul_consmodule_infos.find(name='span',string='上映时间') if tag_ul_consmodule_infos is not None else Nonemovie_release_date = tag_li_em_release_date.find_next_sibling().text.strip() if tag_li_em_release_date is not None else Nonetag_li_em_director = tag_ul_consmodule_infos.select_one('li > em > a') if tag_ul_consmodule_infos is not None else Nonemovie_director = tag_li_em_director.text.strip() if tag_li_em_director is not None else Nonetag_ul_consmodule_infos_r = tag_div_left_top.select_one('ul.consModule_infos.consModule_infos_r.fl') if tag_div_left_top is not None else Nonetag_alternative_titles = tag_ul_consmodule_infos_r.select_one('li > em') if tag_ul_consmodule_infos_r is not None else Nonemovie_alternative_titles = tag_alternative_titles.text if tag_alternative_titles is not None else Nonetag_adaptation_source = tag_ul_consmodule_infos_r.find(name='span',string='改编来源') if tag_ul_consmodule_infos_r is not None else Nonemovie_adaptation_source = tag_adaptation_source.find_next_sibling().text if tag_adaptation_source is not None else Nonetag_screenwriter = tag_ul_consmodule_infos_r.select_one('li > em > a') if tag_ul_consmodule_infos_r is not None else Nonemovie_screenwriter = tag_screenwriter.text if tag_screenwriter is not None else Nonetag_lead_actors = soup.select_one('#left_top > div > ul > li')tag_lead_actors_a_list = tag_lead_actors.select('a') if tag_lead_actors is not None else Nonemovie_lead_actors = [tag.text for tag in tag_lead_actors_a_list] if tag_lead_actors_a_list is not None else []tag_plot = soup.select_one('#left_top > ul > li.plotItem.borderStyle > div > a')movie_plot = tag_plot.text if tag_plot is not None else Nonemovie_data_dict = {'movie_id': movie_id,'movie_url': movie_url,'movie_img_url': movie_img_url,'movie_duration': movie_duration,'movie_title': movie_title,'movie_director': movie_director,'movie_release_date': movie_release_date,'movie_status': movie_status,'movie_rating': movie_rating,'movie_genres': movie_genres,'movie_lead_actors': movie_lead_actors,'movie_alternative_titles': movie_alternative_titles,'movie_adaptation_source': movie_adaptation_source,'movie_screenwriter': movie_screenwriter,'movie_plot': movie_plot}i = i + 1print(f'===============================第{i}行数据，解析后的数据如下：===============================')print(movie_data_dict)print('=============================================================================================')movie_data_list.append(movie_data_dict)count = count + 1if count == 200:df = pd.DataFrame(movie_data_list)if not csv_file_path.exists():df.to_csv(csv_file_dir + csv_file_name, index=False, encoding='utf-8-sig')else:df.to_csv(csv_file_dir + csv_file_name, index=False, encoding='utf-8-sig', mode='a', header=False)movie_data_list = []count = 0print(f'===============================解析后的电影数据已保存到 {csv_file_dir + csv_file_name} 文件===============================')if count != 0:df = pd.DataFrame(movie_data_list)df.to_csv(csv_file_dir + csv_file_name, index=False, encoding='utf-8-sig', mode='a', header=False)print(f'===============================解析后的电影数据已全部保存到 {csv_file_dir + csv_file_name} 文件===============================')if __name__ == '__main__':parse_detail_html_to_csv()

保存后的文件内容如下图：

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四、数据清洗与存储代码实现

import re
from datetime import datetimeimport pandas as pd
from sqlalchemy import create_enginedef read_csv_to_df(file_path):# 加载CSV文件到DataFramereturn pd.read_csv(file_path, encoding='utf-8')def contains_hours(text):if pd.isna(text):  # 检查是否为 NaN 或 Nonereturn Falsepattern = r'\d+\s*(小时|h|hours?|hrs?)'return bool(re.search(pattern, text))def convert_to_minutes(duration_str):parts = str(duration_str).replace('小时', ' ').replace('分钟', '').split()hours = int(parts[0]) if len(parts) > 0 else 0minutes = int(parts[1]) if len(parts) > 1 else 0return hours * 60 + minutes# 定义一个函数来清理和标准化日期
def clean_and_standardize_date(date_str):date_str_cleaned = str(date_str)# 移除括号及其内容if '(' in date_str_cleaned:date_str_cleaned = date_str.split('(')[0]# 尝试匹配并解析完整的日期格式if "年" in date_str_cleaned and "月" in date_str_cleaned and "日" in date_str_cleaned:date_obj = datetime.strptime(date_str_cleaned, "%Y年%m月%d日")elif "年" in date_str_cleaned and "月" in date_str_cleaned:date_obj = datetime.strptime(date_str_cleaned, "%Y年%m月")date_obj = date_obj.replace(day=1)  # 设置为该月的第一天elif "年" in date_str_cleaned:date_obj = datetime.strptime(date_str_cleaned, "%Y年")date_obj = date_obj.replace(month=1, day=1)  # 设置为该年的第一天else:return None  # 如果不符合任何已知模式，则返回 None 或其他默认值return date_obj.strftime("%Y-%m-%d")  # 返回标准格式的字符串# 定义函数：清理和转换数据格式
def clean_and_transform(df):# 筛选出电影状态为“已上映”的数据df = df[df['movie_status'] == '已上映']# 删除电影标题为空的行df.dropna(subset=['movie_title'], inplace=True)# 删除id相同的数据df.drop_duplicates(subset=['movie_id'], inplace=True)# 电影时长字段处理df['movie_duration'] = df['movie_duration'].apply(lambda x: x if contains_hours(x) else None)if df['movie_duration'].isnull().sum() != 0:df['movie_duration'] = df['movie_duration'].fillna(method='ffill')df['movie_duration'] = df['movie_duration'].apply(convert_to_minutes)# 发布日期字段处理df['movie_release_date'] = df['movie_release_date'].apply(clean_and_standardize_date)if df['movie_release_date'].isnull().sum() != 0:df['movie_release_date'] = df['movie_release_date'].fillna(method='ffill')# 评分字段处理df['movie_rating'] = df['movie_rating'].astype('float').round(1)if df['movie_rating'].isnull().sum() != 0:df['movie_rating'] = df['movie_rating'].interpolate()# 类型字段处理if df['movie_genres'].isnull().sum() != 0:df['movie_genres'] = df['movie_genres'].fillna(method='ffill')# 其他空值字段处理df = df.fillna('未知')return dfdef save_df_to_db(df):# 设置数据库连接信息db_user = 'root'db_password = 'zxcvbq'db_host = '127.0.0.1'  # 或者你的数据库主机地址db_port = '3306'  # MySQL默认端口是3306db_name = 'movie1905'# 创建数据库引擎engine = create_engine(f'mysql+mysqlconnector://{db_user}:{db_password}@{db_host}:{db_port}/{db_name}')# 将df写入MySQL表df.to_sql(name='movie1905_china', con=engine, if_exists='replace', index=False)print("所有csv文件的数据已成功清洗并写入MySQL数据库")if __name__ == '__main__':csv_file = r'./data_csv/detail_1905movie_dataset.csv'dataframe = read_csv_to_df(csv_file)dataframe = clean_and_transform(dataframe)save_df_to_db(dataframe)

清洗并存储后的部分数据如下图所示：