将嵌套字典转换为表/父子结构，Python 3.6

Question

想从下面的代码转换嵌套的Dictionary。

import requests
from bs4 import BeautifulSoup

url = 'https://www.bundesbank.de/en/statistics/time-series-databases/time-series-databases/743796/openAll?treeAnchor=BANKEN&statisticType=BBK_ITS'
result = requests.get(url)
soup = BeautifulSoup(result.text, 'html.parser')

def get_child_nodes(parent_node):
    node_name = parent_node.a.get_text(strip=True)

    result = {"name": node_name, "children": []}

    children_list = parent_node.find('ul', recursive=False)
    if not children_list:
    return result

    for child_node in children_list('li', recursive=False):
    result["children"].append(get_child_nodes(child_node))

    return result

Data_Dict = get_child_nodes(soup.find("div", class_="statisticTree"))

是否可以导出如图所示的Parent-Child？

以上代码来自@alecxe的答案：Fetch complete List of Items using BeautifulSoup, Python 3.6

我尝试过，但是太复杂了，难以理解，请提供帮助。

字典：http://s000.tinyupload.com/index.php?file_id=97731876598977568058

示例字典数据：

{"name": "Banks", "children": [{"name": "Banks", "children": [{"name": "Balance sheet items", "children": 
[{"name": "Minimum reserves", "children": [{"name": "Reserve maintenance in the euro area", "children": []}, {"name": "Reserve maintenance in Germany", "children": []}]}, 

{"name": "Bank Lending Survey (BLS) - Results for Germany", "children": [{"name": "Lending", "children": [{"name": "Enterprises", "children": [{"name": "Changes over the past three months", "children": [{"name": "Credit standards and explanatory factors", "children": [{"name": "Overall", "children": []}, {"name": "Loans to small and medium-sized enterprises", "children": []}, {"name": "Loans to large enterprises", "children": []}, {"name": "Short-term loans", "children": []}, {"name": "Long-term loans", "children": []}]}, {"name": "Terms and conditions and explanatory factors", "children": [{"name": "Overall", "children": [{"name": "Overall terms and conditions and explanatory factors", "children": []}, {"name": "Margins on average loans and explanatory factors", "children": []}, {"name": "Margins on riskier loans and explanatory factors", "children": []}, {"name": "Non-interest rate charges", "children": []}, {"name": "Size of the loan or credit line", "children": []}, {"name": "Collateral requirements", "children": []}, {"name": "Loan covenants", "children": []}, {"name": "Maturity", "children": []}]}, {"name": "Loans to small and medium-sized enterprises", "children": []}, {"name": "Loans to large enterprises", "children": []}]}, {"name": "Share of enterprise rejected loan applications", "children": []}]}, {"name": "Expected changes over the next three months", "children": [{"name": "Credit standards", "children": []}]}]}, {"name": "Households", "children": [{"name": "Changes over the past three months", "children": [{"name": "Credit standards and explanatory factors", "children": [{"name": "Loans for house purchase", "children": []}, {"name": "Consumer credit and other lending", "children": []}]}, 

Answer 1

您可以使用递归函数来处理。

def get_pairs(data, parent=''):
    rv = [(data['name'], parent)]
    if not data['children']:
        return rv
    else:
        for d in data['children']:    
            rv.extend(get_pairs(d, parent=data['name']))
    return rv

Data_Dict = get_child_nodes(soup.find("div", class_="statisticTree"))

pairs = get_pairs(Data_Dict)

然后，您可以选择创建DataFrame或立即导出到csv，如示例输出所示。要创建一个DataFrame，我们可以简单地做：

df = pd.DataFrame(get_pairs(Data_Dict), columns=['Name', 'Parent'])

提供：

                                             Name               Parent
0                                           Banks                     
1                                           Banks                Banks
2                             Balance sheet items                Banks
3                                Minimum reserves  Balance sheet items
4            Reserve maintenance in the euro area     Minimum reserves
                                          ...                  ...
3890  Number of transactions per type of terminal  Payments statistics
3891   Value of transactions per type of terminal  Payments statistics
3892                   Number of OTC transactions  Payments statistics
3893                    Value of OTC transactions  Payments statistics
3894                        Issuance of banknotes  Payments statistics

[3895 rows x 2 columns]

或者要输出到csv，我们可以使用csv内置库：

csv

输出：

import csv

with open('out.csv', 'w', newline='') as f:
    writer = csv.writer(f, delimiter=',')
    writer.writerow(('Name', 'Parent'))
    for pair in pairs:
        writer.writerow(pair)