Question

我希望找到我面临的问题的答案。我目前正在尝试为一个项目抓取财务数据，并且正在创建 DataFrame 来存储我的数据以供下游操作的最后一步。

我的问题是我创建了一个键值对字典，其中键是公司符号，值对是一个列表，其中包含与该公司的市场数据相对应的键值对嵌套字典.以下是原始数据的示例。

{'MMM': [{'Market Cap': '102.234B'},
  {'Beta (5Y Monthly)': '0.95'},
  {'PE Ratio (TTM)': '19.09'},
  {'EPS (TTM)': '9.25'},
  {'Earnings Date': 'Apr 26, 2021 - Apr 30, 2021'},
  {'Forward Dividend & Yield': '5.92 (3.35%)'},
  {'Ex-Dividend Date': 'Feb 11, 2021'},
  {'1y Target Est': '183.43'}],
 'AXP': [{'Market Cap': '106.104B'},
  {'Beta (5Y Monthly)': '1.28'},
  {'PE Ratio (TTM)': '34.94'},
  {'EPS (TTM)': '3.77'},
  {'Earnings Date': 'Apr 22, 2021 - Apr 26, 2021'},
  {'Forward Dividend & Yield': '1.72 (1.31%)'},
  {'Ex-Dividend Date': 'Jan 07, 2021'},
  {'1y Target Est': '130.32'}],
 'AMGN': [{'Market Cap': '133.816B'},
  {'Beta (5Y Monthly)': '0.73'},
  {'PE Ratio (TTM)': '18.82'},
  {'EPS (TTM)': '12.31'},

我尝试使用 .from_dict 和索引方向创建数据帧，但这是我得到的结果..

    0   1   2   3   4   5   6   7
MMM {'Market Cap': '102.234B'}  {'Beta (5Y Monthly)': '0.95'}   {'PE Ratio (TTM)': '19.09'} {'EPS (TTM)': '9.25'}   {'Earnings Date': 'Apr 26, 2021 - Apr 30, 2021'}    {'Forward Dividend & Yield': '5.92 (3.35%)'}    {'Ex-Dividend Date': 'Feb 11, 2021'}    {'1y Target Est': '183.43'}
AXP {'Market Cap': '106.104B'}  {'Beta (5Y Monthly)': '1.28'}   {'PE Ratio (TTM)': '34.94'} {'EPS (TTM)': '3.77'}   {'Earnings Date': 'Apr 22, 2021 - Apr 26, 2021'}    {'Forward Dividend & Yield': '1.72 (1.31%)'}    {'Ex-Dividend Date': 'Jan 07, 2021'}    {'1y Target Est': '130.32'}
AMGN    {'Market Cap': '133.816B'}  {'Beta (5Y Monthly)': '0.73'}   {'PE Ratio (TTM)': '18.82'} {'EPS (TTM)': '12.31'}  {'Earnings Date': 'Apr 28, 2021 - May 03, 2021'}    {'Forward Dividend & Yield': '7.04 (3.04%)'}    {'Ex-Dividend Date': 'Feb 11, 2021'}    {'1y Target Est': '254.20'}

我将如何提取键/值对，以便键是列名而值是数据点？任何帮助将不胜感激...谢谢

Answer 1

您可以先展平嵌套字典并传递给 DataFrame.from_dict：

d = {k: {k1: v1 for y in v for k1, v1 in y.items()} for k, v in d.items()}

df = pd.DataFrame.from_dict(d, orient='index')

将嵌套值转换为 Series 然后将 concat 与转置一起使用的解决方案：

df = pd.concat({k: pd.concat([pd.Series(y) for y in v]) for k, v in d.items()}, axis=1).T
print (df)
    Market Cap Beta (5Y Monthly) PE Ratio (TTM) EPS (TTM)  \
MMM   102.234B              0.95          19.09      9.25   
AXP   106.104B              1.28          34.94      3.77   

                   Earnings Date Forward Dividend & Yield Ex-Dividend Date  \
MMM  Apr 26, 2021 - Apr 30, 2021             5.92 (3.35%)     Feb 11, 2021   
AXP  Apr 22, 2021 - Apr 26, 2021             1.72 (1.31%)     Jan 07, 2021   

    1y Target Est  
MMM        183.43  
AXP        130.32

从包含键和字典项嵌套列表的字典创建 Pandas DataFrame

1 个答案: