pip install scrapy fake-useragent requests selenium virtualenv -i https://pypi.douban.com/simple

1.1 創建虛擬環境

切換到指定目錄創建

virtualenv .venv

創建完記得激活虛擬環境

1.2 創建項目

scrapy startproject 項目名稱

1.3 使用pycharm打開項目，將創建的虛擬環境配置到項目中來1.4 創建京東spider

scrapy genspider 爬蟲名稱 url

1.5 修改允許訪問的域名，刪除https：

爬取數據的思路是先獲取首頁的基本信息，在獲取詳情頁商品詳細信息；爬取京東數據時，只返回40條數據，這里，作者使用selenium，在scrapy框架中編寫下載器中間件，返回頁面所有數據。爬取的字段分別是：

商品價格

商品評數

商品店家

商品SKU（京東可直接搜索到對應的產品）

商品標題

商品詳細信息

import reimport scrapyfrom lianjia.items import jd_detailItemclass JiComputerDetailSpider(scrapy.Spider): name = ’ji_computer_detail’ allowed_domains = [’search.jd.com’, ’item.jd.com’] start_urls = [’https://search.jd.com/Search?keyword=%E7%AC%94%E8%AE%B0%E6%9C%AC%E7%94%B5%E8%84%91&suggest=1.def.0.base&wq=%E7%AC%94%E8%AE%B0%E6%9C%AC%E7%94%B5%E8%84%91&page=1&s=1&click=0’] def parse(self, response):lls = response.xpath(’//ul[@class='gl-warp clearfix']/li’)for ll in lls: item = jd_detailItem() computer_price = ll.xpath(’.//div[@class='p-price']/strong/i/text()’).extract_first() computer_commit = ll.xpath(’.//div[@class='p-commit']/strong/a/text()’).extract_first() computer_p_shop = ll.xpath(’.//div[@class='p-shop']/span/a/text()’).extract_first() item[’computer_price’] = computer_price item[’computer_commit’] = computer_commit item[’computer_p_shop’] = computer_p_shop meta = {’item’: item } shop_detail_url = ll.xpath(’.//div[@class='p-img']/a/@href’).extract_first() shop_detail_url = ’https:’ + shop_detail_url yield scrapy.Request(url=shop_detail_url, callback=self.detail_parse, meta=meta)for i in range(2, 200, 2): next_page_url = f’https://search.jd.com/Search?keyword=%E7%AC%94%E8%AE%B0%E6%9C%AC%E7%94%B5%E8%84%91&suggest=1.def.0.base&wq=%E7%AC%94%E8%AE%B0%E6%9C%AC%E7%94%B5%E8%84%91&page={i}&s=116&click=0’ yield scrapy.Request(url=next_page_url, callback=self.parse) def detail_parse(self, response):item = response.meta.get(’item’)computer_sku = response.xpath(’//a[@class='notice J-notify-sale']/@data-sku’).extract_first()item[’computer_sku’] = computer_skucomputer_title = response.xpath(’//div[@class='sku-name']/text()’).extract_first().strip()computer_title = ’’.join(re.findall(’S’, computer_title))item[’computer_title’] = computer_titlecomputer_detail = response.xpath(’string(//ul[@class='parameter2 p-parameter-list'])’).extract_first().strip()computer_detail = ’’.join(re.findall(’S’, computer_detail))item[’computer_detail’] = computer_detailyield item四、item

class jd_detailItem(scrapy.Item): # define the fields for your item here like: computer_sku = scrapy.Field() computer_price = scrapy.Field() computer_title = scrapy.Field() computer_commit = scrapy.Field() computer_p_shop = scrapy.Field() computer_detail = scrapy.Field()五、setting

import randomfrom fake_useragent import UserAgentua = UserAgent()USER_AGENT = ua.randomROBOTSTXT_OBEY = FalseDOWNLOAD_DELAY = random.uniform(0.5, 1)DOWNLOADER_MIDDLEWARES = { ’lianjia.middlewares.jdDownloaderMiddleware’: 543}ITEM_PIPELINES = { ’lianjia.pipelines.jd_csv_Pipeline’: 300}六、pipelines

class jd_csv_Pipeline: # def process_item(self, item, spider): # return item def open_spider(self, spider):self.fp = open(’./jd_computer_message.xlsx’, mode=’w+’, encoding=’utf-8’)self.fp.write(’computer_skutcomputer_titletcomputer_p_shoptcomputer_pricetcomputer_committcomputer_detailn’) def process_item(self, item, spider):# 寫入文件try: line = ’t’.join(list(item.values())) + ’n’ self.fp.write(line) return itemexcept: pass def close_spider(self, spider):# 關閉文件self.fp.close()七、middlewares

class jdDownloaderMiddleware: def process_request(self, request, spider):# 判斷是否是ji_computer_detail的爬蟲# 判斷是否是首頁if spider.name == ’ji_computer_detail’ and re.findall(f’.*(item.jd.com).*’, request.url) == []: options = ChromeOptions() options.add_argument('--headless') driver = webdriver.Chrome(options=options) driver.get(request.url) for i in range(0, 15000, 5000):driver.execute_script(f’window.scrollTo(0, {i})’)time.sleep(0.5) body = driver.page_source.encode() time.sleep(1) return HtmlResponse(url=request.url, body=body, request=request)return None八、使用jupyter進行簡單的處理和分析

其他文件：百度停用詞庫、簡體字文件下載第三方包

!pip install seaborn jieba wordcloud PIL -i https://pypi.douban.com/simple

8.1導入第三方包

import reimport osimport jiebaimport wordcloudimport pandas as pdimport numpy as npfrom PIL import Imageimport seaborn as snsfrom docx import Documentfrom docx.shared import Inchesimport matplotlib.pyplot as pltfrom pandas import DataFrame,Series

8.2設置可視化的默認字體和seaborn的樣式

sns.set_style(’darkgrid’)plt.rcParams[’font.sans-serif’] = [’SimHei’]plt.rcParams[’axes.unicode_minus’] = False

8.3讀取數據

df_jp = pd.read_excel(’./jd_shop.xlsx’)

8.4篩選Inteli5、i7、i9處理器數據

def convert_one(s): if re.findall(f’.*?(i5).*’, str(s)) != []:return re.findall(f’.*?(i5).*’, str(s))[0] elif re.findall(f’.*?(i7).*’, str(s)) != []:return re.findall(f’.*?(i7).*’, str(s))[0] elif re.findall(f’.*?(i9).*’, str(s)) != []:return re.findall(f’.*?(i9).*’, str(s))[0]df_jp[’computer_intel’] = df_jp[’computer_detail’].map(convert_one)

8.5篩選筆記本電腦的屏幕尺寸范圍

def convert_two(s): if re.findall(f’.*?(d+.d+英寸-d+.d+英寸).*’, str(s)) != []:return re.findall(f’.*?(d+.d+英寸-d+.d+英寸).*’, str(s))[0]df_jp[’computer_in’] = df_jp[’computer_detail’].map(convert_two)

8.6將評論數轉化為整形

def convert_three(s): if re.findall(f’(d+)萬+’, str(s)) != []:number = int(re.findall(f’(d+)萬+’, str(s))[0]) * 10000return number elif re.findall(f’(d+)+’, str(s)) != []:number = re.findall(f’(d+)+’, str(s))[0]return numberdf_jp[’computer_commit’] = df_jp[’computer_commit’].map(convert_three)

8.7篩選出需要分析的品牌

def find_computer(name, s): sr = re.findall(f’.*({name}).*’, str(s))[0] return srdef convert(s): if re.findall(f’.*(聯想).*’, str(s)) != []:return find_computer(’聯想’, s) elif re.findall(f’.*(惠普).*’, str(s)) != []:return find_computer(’惠普’, s) elif re.findall(f’.*(華為).*’, str(s)) != []:return find_computer(’華為’, s) elif re.findall(f’.*(戴爾).*’, str(s)) != []:return find_computer(’戴爾’, s) elif re.findall(f’.*(華碩).*’, str(s)) != []:return find_computer(’華碩’, s) elif re.findall(f’.*(小米).*’, str(s)) != []:return find_computer(’小米’, s) elif re.findall(f’.*(榮耀).*’, str(s)) != []:return find_computer(’榮耀’, s) elif re.findall(f’.*(神舟).*’, str(s)) != []:return find_computer(’神舟’, s) elif re.findall(f’.*(外星人).*’, str(s)) != []:return find_computer(’外星人’, s)df_jp[’computer_p_shop’] = df_jp[’computer_p_shop’].map(convert)

8.8刪除指定字段為空值的數據

for n in [’computer_price’, ’computer_commit’, ’computer_p_shop’, ’computer_sku’, ’computer_detail’, ’computer_intel’, ’computer_in’]: index_ls = df_jp[df_jp[[n]].isnull().any(axis=1)==True].index df_jp.drop(index=index_ls, inplace=True)

8.9查看各品牌的平均價格

plt.figure(figsize=(10, 8), dpi=100)ax = sns.barplot(x=’computer_p_shop’, y=’computer_price’, data=df_jp.groupby(by=’computer_p_shop’)[[’computer_price’]].mean().reset_index())for index,row in df_jp.groupby(by=’computer_p_shop’)[[’computer_price’]].mean().reset_index().iterrows(): ax.text(row.name,row[’computer_price’] + 2,round(row[’computer_price’],2),color='black',ha='center')ax.set_xlabel(’品牌’)ax.set_ylabel(’平均價格’)ax.set_title(’各品牌平均價格’)boxplot_fig = ax.get_figure()boxplot_fig.savefig(’各品牌平均價格.png’, dpi=400)

8.10 查看各品牌的價格區間

plt.figure(figsize=(10, 8), dpi=100)ax = sns.boxenplot(x=’computer_p_shop’, y=’computer_price’, data=df_jp.query(’computer_price>500’))ax.set_xlabel(’品牌’)ax.set_ylabel(’價格區間’)ax.set_title(’各品牌價格區間’)boxplot_fig = ax.get_figure()boxplot_fig.savefig(’各品牌價格區間.png’, dpi=400)

8.11 查看價格與評論數的關系

df_jp[’computer_commit’] = df_jp[’computer_commit’].astype(’int64’)ax = sns.jointplot(x='computer_commit', y='computer_price', data=df_jp, kind='reg', truncate=False,color='m', height=10)ax.fig.savefig(’評論數與價格的關系.png’)

8.12 查看商品標題里出現的關鍵詞

import imageio# 將特征轉換為列表ls = df_jp[’computer_title’].to_list()# 替換非中英文的字符feature_points = [re.sub(r’[^a-zA-Zu4E00-u9FA5]+’,’ ’,str(feature)) for feature in ls]# 讀取停用詞stop_world = list(pd.read_csv(’./百度停用詞表.txt’, engine=’python’, encoding=’utf-8’, names=[’stopwords’])[’stopwords’])feature_points2 = []for feature in feature_points: # 遍歷每一條評論 words = jieba.lcut(feature) # 精確模式，沒有冗余.對每一條評論進行jieba分詞 ind1 = np.array([len(word) > 1 for word in words]) # 判斷每個分詞的長度是否大于1 ser1 = pd.Series(words) ser2 = ser1[ind1] # 篩選分詞長度大于1的分詞留下 ind2 = ~ser2.isin(stop_world) # 注意取反負號 ser3 = ser2[ind2].unique() # 篩選出不在停用詞表的分詞留下,并去重 if len(ser3) > 0:feature_points2.append(list(ser3))# 將所有分詞存儲到一個列表中wordlist = [word for feature in feature_points2 for word in feature]# 將列表中所有的分詞拼接成一個字符串feature_str = ’ ’.join(wordlist) # 標題分析font_path = r’./simhei.ttf’shoes_box_jpg = imageio.imread(’./home.jpg’)wc=wordcloud.WordCloud( background_color=’black’, mask=shoes_box_jpg, font_path = font_path, min_font_size=5, max_font_size=50, width=260, height=260,)wc.generate(feature_str)plt.figure(figsize=(10, 8), dpi=100)plt.imshow(wc)plt.axis(’off’)plt.savefig(’標題提取關鍵詞’)

8.13 篩選價格在4000到5000，聯想品牌、處理器是i5、屏幕大小在15寸以上的數據并查看價格

df_jd_query = df_jp.loc[(df_jp[’computer_price’] <=5000) & (df_jp[’computer_price’]>=4000) & (df_jp[’computer_p_shop’]=='聯想') & (df_jp[’computer_intel’]=='i5') & (df_jp[’computer_in’]=='15.0英寸-15.9英寸'), :].copy()plt.figure(figsize=(20, 10), dpi=100)ax = sns.barplot(x=’computer_sku’, y=’computer_price’, data=df_jd_query)ax.set_xlabel(’聯想品牌SKU’)ax.set_ylabel(’價格’)ax.set_title(’酷睿i5處理器屏幕15寸以上各SKU的價格’)boxplot_fig = ax.get_figure()boxplot_fig.savefig(’酷睿i5處理器屏幕15寸以上各SKU的價格.png’, dpi=400)

8.14 篩選價格在4000到5000，戴爾品牌、處理器是i7、屏幕大小在15寸以上的數據并查看價格

df_jp_daier = df_jp.loc[(df_jp[’computer_price’] <=5000) & (df_jp[’computer_price’]>=4000) & (df_jp[’computer_p_shop’]=='戴爾') & (df_jp[’computer_intel’]=='i7') & (df_jp[’computer_in’]=='15.0英寸-15.9英寸'), :].copy()plt.figure(figsize=(10, 8), dpi=100)ax = sns.barplot(x=’computer_sku’, y=’computer_price’, data=df_jp_daier)ax.set_xlabel(’戴爾品牌SKU’)ax.set_ylabel(’價格’)ax.set_title(’酷睿i7處理器屏幕15寸以上各SKU的價格’)boxplot_fig = ax.get_figure()boxplot_fig.savefig(’酷睿i7處理器屏幕15寸以上各SKU的價格.png’, dpi=400)

8.15 不同Intel處理器品牌的價格

plt.figure(figsize=(10, 8), dpi=100)ax = sns.barplot(x=’computer_p_shop’, y=’computer_price’, data=df_jp, hue=’computer_intel’)ax.set_xlabel(’品牌’)ax.set_ylabel(’價格’)ax.set_title(’不同酷睿處理器品牌的價格’)boxplot_fig = ax.get_figure()boxplot_fig.savefig(’不同酷睿處理器品牌的價格.png’, dpi=400)

8.16 不同尺寸品牌的價格

plt.figure(figsize=(10, 8), dpi=100)ax = sns.barplot(x=’computer_p_shop’, y=’computer_price’, data=df_jp, hue=’computer_in’)ax.set_xlabel(’品牌’)ax.set_ylabel(’價格’)ax.set_title(’不同尺寸品牌的價格’)boxplot_fig = ax.get_figure()boxplot_fig.savefig(’不同尺寸品牌的價格.png’, dpi=400)

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