Question

我从https://data.gov.au/dataset?organization=reservebankofaustralia&_groups_limit=0&groups=business提取数据得到了我想要的输出，但现在的问题是：我得到的输出是商业支持......和奥斯的储备银行....，不是完整的文字，我想要打印整个文本而不是“.... ...“ 对全部。我在jezrael的回答中替换了第9行和第10行，请使用代码org = soup.find_all('a', {'class':'nav-item active'})[0].get('title') groups = soup.find_all('a', {'class':'nav-item active'})[1].get('title')查看Fetching content from html and write fetched content in a specific format in CSV 。我正在单独运行它并得到错误：列表索引超出范围。我应该用什么来提取完整的句子？我也尝试过： org = soup.find_all('span',class_="filtered pill")，当我单独运行时，它给出了字符串类型的答案，但无法使用整个代码运行。

Answer 1

我猜你是想这样做的。每个链接都有title属性。所以在这里我只是检查是否存在任何title属性，如果是，那么我只是打印它。

有空行，因为title=""的链接很少，所以你可以使用条件语句来避免这种情况，然后从中获取所有标题。

>>> l = soup.find_all('a')
>>> for i in l:
...     if i.has_attr('title'):
...             print(i['title'])
... 
Remove
Remove
Reserve Bank of Australia

Business Support and Regulation













Creative Commons Attribution 3.0 Australia
>>>

Answer 2

所有包含较长文字的数据均属于title，文字较短。所以添加双if：

for i in webpage_urls:
    wiki2 = i
    page= urllib.request.urlopen(wiki2)
    soup = BeautifulSoup(page, "lxml")

    lobbying = {}
    #always only 2 active li, so select first by [0]  and second by [1]
    l = soup.find_all('li', class_="nav-item active")

    org = l[0].a.get('title')
    if org == '':
        org = l[0].span.get_text()

    groups = l[1].a.get('title')
    if groups == '':
        groups = l[1].span.get_text()

    data2 = soup.find_all('h3', class_="dataset-heading")
    for element in data2:
        lobbying[element.a.get_text()] = {}
    data2[0].a["href"]
    prefix = "https://data.gov.au"
    for element in data2:
        lobbying[element.a.get_text()]["link"] = prefix + element.a["href"]
        lobbying[element.a.get_text()]["Organisation"] = org
        lobbying[element.a.get_text()]["Group"] = groups

        #print(lobbying)
        df = pd.DataFrame.from_dict(lobbying, orient='index') \
               .rename_axis('Titles').reset_index()
        dfs.append(df)

df = pd.concat(dfs, ignore_index=True)
df1 = df.drop_duplicates(subset = 'Titles').reset_index(drop=True)

df1['Organisation'] = df1['Organisation'].str.replace('\(\d+\)', '')
df1['Group'] = df1['Group'].str.replace('\(\d+\)', '')

print (df1.head())

                                              Titles  \
0                                     Banks – Assets   
1  Consolidated Exposures – Immediate and Ultimat...   
2  Foreign Exchange Transactions and Holdings of ...   
3  Finance Companies and General Financiers – Sel...   
4                   Liabilities and Assets – Monthly   

                                                link  \
0           https://data.gov.au/dataset/banks-assets   
1  https://data.gov.au/dataset/consolidated-expos...   
2  https://data.gov.au/dataset/foreign-exchange-t...   
3  https://data.gov.au/dataset/finance-companies-...   
4  https://data.gov.au/dataset/liabilities-and-as...   

                Organisation                            Group  
0  Reserve Bank of Australia  Business Support and Regulation  
1  Reserve Bank of Australia  Business Support and Regulation  
2  Reserve Bank of Australia  Business Support and Regulation  
3  Reserve Bank of Australia  Business Support and Regulation  
4  Reserve Bank of Australia  Business Support and Regulation

使用beautifulsoup

2 个答案: