通过R中的部分匹配来合并两个数据帧

Question

我想按名称合并两个数据框；但是，两个数据帧之间的名称略有不同。有没有办法通过部分匹配来合并这两个数据帧？我已经尝试过其他帖子的答案，但没有得到我需要的结果。谢谢

#Create data frames
df1 <- data.frame(
  "Attending" = c("Kokabi, Nima", "Tong, Frank Charles","Devireddy, Chandan",
                  "Greenbaum, Adam B","Amin, Dina"),
  "Outcome" = rep(1, times = 5),stringsAsFactors = F)

df2 <- data.frame(
  "Credentialed" = c("Kokabi, Nima, MD","Tong, Frank Charles, MD",
                     "Devireddy, Chandanreddy M, MD", "Greenbaum, Adam Brett, MD",
                     "Amin, Dina, DDS"),
  "Status" = rep("Active", times = 5),stringsAsFactors = F)

#Desired result
final <- data.frame(
  "Attending" = c("Kokabi, Nima", "Tong, Frank Charles","Devireddy, 
Chandan","Greenbaum, Adam B","Amin, Dina"),
  "Outcome" = rep(1, times = 5),
  "Credentialed" = c("Kokabi, Nima, MD","Tong, Frank Charles, 
MD","Devireddy, Chandanreddy M, MD", "Greenbaum, Adam Brett, MD","Amin, 
Dina, DDS"),
  "Status" = rep("Active", times = 5)
)

head(final)

Answer 1

这是使用import json import re import scrapy PRODUCTS_XPATH = "//div[@class='col-md-3']//a/@href" class Product: def __init__(self, response): self.response = response def get_brand_name(self): brand_name = self.response.xpath( "normalize-space(//*[@class='product-brand-name-details']/text())" ).extract() if not brand_name[0]: brand_name = self.response.xpath( "normalize-space(//h3[@class='font-weight-bold']/text())" ).extract() return brand_name[0] if brand_name else 'Could not get product brand name.' def get_brand_name_details(self): brand_name_details = self.response.xpath( "normalize-space(//*[@class='product-name-details']/text())" ).extract() if not brand_name_details[0]: brand_name_details = self.response.xpath( "normalize-space(//h1[@class='title font-weight-bold']/text())" ).extract() return brand_name_details[0] if brand_name_details else 'Could not get product brand name details.' def get_real_category(self): return self.response.meta.get('product_category') def get_sku_details(self): sku_details = self.response.xpath( "normalize-space(//*[@class='product-sku-details']/text())" ).extract() if not sku_details[0]: sku_details = self.response.xpath( "normalize-space(//h5[@class='font-weight-bold']/text())" ).extract() return sku_details[0] if sku_details else 'Could not get product sku details.' def get_short_desc_details(self): short_desc_details = self.response.xpath( "normalize-space(//p[@class='pt-2']/text())" ).extract() return short_desc_details[0] if short_desc_details else 'Could not get product short desc details.' def get_detail_list_price(self): detail_list_price = self.response.xpath( "normalize-space(//*[@class='product-detail-list-price']//text())" ).extract() return detail_list_price[0] if detail_list_price else 'Could not get product detail list price.' def get_price(self): price = self.response.xpath( "normalize-space(//*[@class='price']//text())" ).extract() return price[0] if price else 'Could not get product price.' def get_detail_price_save(self): detail_price_save = self.response.xpath( "normalize-space(//*[@class='product-detail-price-save']//text())" ).extract() return detail_price_save[0] if detail_price_save else 'Could not get product detail price save.' def get_detail_note(self): detail_note = self.response.xpath( "normalize-space(//*[@class='product-detail-note']//text())" ).extract() return detail_note[0] if detail_note else 'Could not get product detail note.' def get_detail_long_desc(self): detail_long_descriptions = self.response.xpath( "//*[@id='desc']/node()" ).extract() detail_long_desc = ''.join([x.strip() for x in detail_long_descriptions if x.strip()]) return detail_long_desc if detail_long_desc else 'Could not get product detail long desc.' def get_image(self): image = self.response.xpath( "normalize-space(//*[@id='mainContent_imgDetail']/@src)" ).extract() return f'https://bannersolutions.com{image[0]}' if image else 'Could not get product image.' def get_pieces_in_stock(self): pieces_in_stock = self.response.xpath( "normalize-space(//*[@class='badge-success']//text())" ).extract() return pieces_in_stock[0] if pieces_in_stock else 'Unknown pieces in stock.' def get_meta_description(self): meta_description = self.response.xpath( "normalize-space(//*[@name='description']/@content)" ).extract() return meta_description[0] if meta_description else 'Could not get product meta description.' def to_json(self): return { 'product_brand_name_details': self.get_brand_name_details(), 'product_brand_name': self.get_brand_name(), 'product_category': self.get_real_category(), 'product_sku_details': self.get_sku_details(), 'product_short_desc_details': self.get_short_desc_details(), 'product_detail_list_price': self.get_detail_list_price(), 'product_price': self.get_price(), 'product_detail_price_save': self.get_detail_price_save(), 'product_detail_note': self.get_detail_note(), 'product_detail_long_desc': self.get_detail_long_desc(), 'product_image': self.get_image(), 'product_in_stock': self.get_pieces_in_stock(), 'product_meta_description': self.get_meta_description() } class BannerSolutionsSpider(scrapy.Spider): name = 'bannersolutions' start_urls = ['https://bannersolutions.com/Sitemap'] allowed_domains = ['bannersolutions.com'] def start_crawl(self, response): for url in self.start_urls: yield scrapy.Request(url) def parse(self, response): for category in response.xpath('(//div[@class="col-md-3"])[1]/ul/li'): main_category_name = category.xpath('./a/text()').get() sub_category_name = category.xpath('./ul/li/a/text()').get() category_url = category.xpath('./ul/li/a/@href').get() if category_url: yield scrapy.Request(f'https://bannersolutions.com{category_url}', callback=self.parse_categories, meta={'product_category': f'{main_category_name}/{sub_category_name}'}) def parse_categories(self, response): title = response.xpath('//h1[@class="title"]/text()').get() products_in_category = re.match(r'.*\((\d+)\)', title).group(1) no_of_requests = int(products_in_category) // 8 + 1 in_cat_id = response.url.split('/')[-1] for i in range(1, no_of_requests): payload = { 'pageIndex': str(i), 'inViewType': 'grid', 'inPageSize': '8', 'inCatID': in_cat_id, 'inFilters': '', 'inSortType': '' } yield scrapy.Request( 'https://bannersolutions.com/catalog.aspx/GetProducts', method='POST', headers={"content-type": "application/json"}, body=json.dumps(payload), callback=self.parse_plm, meta={'product_category': response.meta.get('product_category')} ) def parse_plm(self, response): products_str_html = json.loads(response.body).get('d') product_url = scrapy.selector.Selector(text=products_str_html).xpath( '//div[@class="product-image-container"]//a/@href' ).get() yield scrapy.Request( f'https://bannersolutions.com{product_url}', callback=self.parse_product, meta={'product_category': response.meta.get('product_category')} ) def parse_product(self, response): product = Product(response).to_json() yield product 的可能解决方案。

grep

请注意，建议您在data.frame调用中设置df1$Credentialed <- grep(paste(df1$Attending,collapse = '|'),df2$Credentialed,value=T) left_join(df1,df2) Joining, by = "Credentialed" Attending Outcome Credentialed Status 1 Kokabi, Nima 1 Kokabi, Nima, MD Active 2 Tong, Frank Charles 1 Tong, Frank Charles, MD Active 3 Devireddy, Chandan 1 Devireddy, Chandanreddy M, MD Active 4 Greenbaum, Adam B 1 Greenbaum, Adam Brett, MD Active 5 Amin, Dina 1 Amin, Dina, DDS Active 。并注意您如何粘贴名称-返回值将由R读取，而不是空格。

stringsAsFactors=F