遍历嵌套字典以创建数据框并添加新的列值

Question

Python 菜鸟，请多多包涵。

我有一个股票信息字典列表。变量名称“json”，我想将其转换为数据框，然后在数据旁边的新列中附加一个带有股票代码的列。见下文。

    json =
    [{'Meta Data': {'1. Information': 'Monthly Prices (open, high, low, close) and Volumes', '2. 
    Symbol': 'AAPL', '3. Last Refreshed': '2021-01-29', '4. Time Zone': 'US/Eastern'}, 'Monthly Time 
    Series': {'2021-01-29': {'1. open': '133.5200', '2. high': '145.0900', '3. low': '126.3820', '4. 
    close': '131.9600', '5. volume': '2239366098'}, '2020-12-31': {'1. open': '121.0100', '2. high': 
    '138.7890', '3. low': '120.0100', '4. close': '132.6900', '5. volume': '2319687808'}}},

    {'Meta Data': {'1. Information': 'Monthly Prices (open, high, low, close) and Volumes', '2. 
    Symbol': 'ZM', '3. Last Refreshed': '2021-01-29', '4. Time Zone': 'US/Eastern'}, 'Monthly Time 
    Series': {'2021-01-29': {'1. open': '340.4000', '2. high': '404.4400', '3. low': '331.1000', '4. 
    close': '372.0700', '5. volume': '121350349'}, '2020-12-31': {'1. open': '434.7200', '2. high': 
    '434.9900', '3. low': '336.1000', '4. close': '337.3200', '5. volume': '150168985'}}}]

我运行以下命令，得到我想要的数据框，除了代码：

    df = [pd.DataFrame.from_dict(i['Monthly Time Series'], orient= 'index').sort_index(axis=1) for i in json]

输出：

    [             1. open   2. high    3. low  4. close   5. volume
    2021-01-29  133.5200  145.0900  126.3820  131.9600  2239366098
    2020-12-31  121.0100  138.7890  120.0100  132.6900  2319687808
    ],
                  1. open   2. high    3. low  4. close  5. volume
    2021-01-29  340.4000  404.4400  331.1000  372.0700  121350349
    2020-12-31  434.7200  434.9900  336.1000  337.3200  150168985]

我想要的是从'2中提取值。 Symbol' 来自 json 并将相应的股票代码附加到相应的数据中，如下所示：

    [             1. open   2. high    3. low  4. close   5. volume  ticker
    2021-01-29  133.5200  145.0900  126.3820  131.9600  2239366098  AAPL
    2020-12-31  121.0100  138.7890  120.0100  132.6900  2319687808  AAPL
    ],
                  1. open   2. high    3. low  4. close  5. volume  ticker
    2021-01-29  340.4000  404.4400  331.1000  372.0700  121350349  ZM
    2020-12-31  434.7200  434.9900  336.1000  337.3200  150168985  ZM

]

Answer 1

更新：

单循环一行执行

df = [ (pd.DataFrame.from_dict(i['Monthly Time Series'] , orient= 'index').sort_index(axis=1).assign(ticker=i['Meta Data']['2.Symbol'])) for i in json]

json 数据：

json =[{
    'Meta Data': {
        '1. Information': 'Monthly Prices (open, high, low, close) and Volumes','2.Symbol': 'AAPL', '3. Last Refreshed': '2021-01-29', '4. Time Zone': 'US/Eastern'},
'Monthly Time Series': {
    '2020-01-29': {
        '1. open': '133.5200', '2. high': '145.0900','3. low': '126.3820', '4. close': '131.9600', '5. volume': '2239366098'
        },  
        '2020-01-30': {'1. open': '121.0100', '2. high': '138.7890', '3. low': '120.0100', 
        '4. close': '132.6900', '5. volume': '2319687808'}
        }
        },
        {
    'Meta Data': {
        '1. Information': 'Monthly Prices (open, high, low, close) and Volumes','2.Symbol': 'ZM', '3. Last Refreshed': '2021-01-01', '4. Time Zone': 'US/Eastern'},
        'Monthly Time Series': {
            '2020-02-02': {'1. open': '133.5200', '2. high': '145.0900','3. low': '126.3820',
            '4. close' : '131.9600', '5. volume': '2239366098'
            },  
        '2020-02-31': 
        {
    '1. open': '121.0100', '2. high': '138.7890', '3. low': '120.0100','4. close' : '132.6900', '5. volume': '2319687808'}
            }
        }]

利用 assign 添加新列

addTimeSeries = lambda i : pd.DataFrame.from_dict(i['Monthly Time Series'] , orient= 'index').sort_index(axis=1)

addVal = lambda i: addTimeSeries(i).assign(ticker=i['Meta Data']['2.Symbol'])
df = [ addVal(i) for i in json]

输出：

[             1. open   2. high    3. low  4. close   5. volume ticker
 2020-01-29  133.5200  145.0900  126.3820  131.9600  2239366098   AAPL
 2020-01-30  121.0100  138.7890  120.0100  132.6900  2319687808   AAPL,
              1. open   2. high    3. low  4. close   5. volume ticker
 2020-02-02  133.5200  145.0900  126.3820  131.9600  2239366098     ZM
 2020-02-31  121.0100  138.7890  120.0100  132.6900  2319687808     ZM]