将JavaScript转换为JSON时遇到问题

rhfm7lfc  于 12个月前  发布在  Java
关注(0)|答案(2)|浏览(177)

我有以下的框架：

2023-06-30      2022-06-30      2021-06-30      2020-06-30
Ordinary Shares Number                              7432000000.0    7464000000.0    7519000000.0    7571000000.0
Share Issued                                        7432000000.0    7464000000.0    7519000000.0    7571000000.0
Net Debt                                           12533000000.0   35850000000.0   43922000000.0   49751000000.0
Total Debt                                         59965000000.0   61270000000.0   67775000000.0   70998000000.0
Tangible Book Value                               128971000000.0   87720000000.0   84477000000.0   67915000000.0
...                                                          ...             ...             ...             ...
Cash Cash Equivalents And Short Term Investments  111262000000.0  104757000000.0  130334000000.0  136527000000.0
Other Short Term Investments                       76558000000.0   90826000000.0  116110000000.0  122951000000.0
Cash And Cash Equivalents                          34704000000.0   13931000000.0   14224000000.0   13576000000.0
Cash Equivalents                                   26226000000.0    5673000000.0    6952000000.0             NaN
Cash Financial                                      8478000000.0    8258000000.0    7272000000.0             NaN

[73 rows x 4 columns]

字符串
我正在尝试将其转换为json，格式如下：

{
    "2023-06-30": {
        "Ordinary Shares Number": "7432000000.0",
        ...
    },
    "2022-06-30": {
        "Ordinary Shares Number": "7464000000.0",
        ...
    },
    "2021-06-30": {
        "Ordinary Shares Number": "7519000000.0",
        ...
    },
    "2020-06-30": {
        "Ordinary Shares Number": "7571000000.0",
        ...
    }
}

型
然而，我试图转换它已经从坏到更坏的范围，所以我真的不知道我在做什么。
我的尝试要么给了我一个非常不受欢迎的JSON格式，要么给了我一个关于时间戳的类型错误：
举例来说：

out = json.dumps({c: dict(zip(balance.index, balance[c])) for c in balance.columns}, indent=4)
print(out)

型
结果：

Traceback (most recent call last):
  File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/usr/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/jesse_b/tools/stonk-db/stonkdb/__main__.py", line 39, in <module>
    main()
  File "/home/jesse_b/tools/stonk-db/stonkdb/__main__.py", line 32, in main
    out = json.dumps({c: dict(zip(balance.index, balance[c])) for c in balance.columns}, indent=4)
  File "/usr/lib/python3.8/json/__init__.py", line 234, in dumps
    return cls(
  File "/usr/lib/python3.8/json/encoder.py", line 201, in encode
    chunks = list(chunks)
  File "/usr/lib/python3.8/json/encoder.py", line 431, in _iterencode
    yield from _iterencode_dict(o, _current_indent_level)
  File "/usr/lib/python3.8/json/encoder.py", line 376, in _iterencode_dict
    raise TypeError(f'keys must be str, int, float, bool or None, '
TypeError: keys must be str, int, float, bool or None, not Timestamp

型

JSON

2条答案

按热度按时间
qpgpyjmq

qpgpyjmq1#

你可以使用dict-comprehension：

import json

# convert the columns to string before (if needed)
# df.columns = df.columns.astype(str)

out = json.dumps({c: dict(zip(df.index, df[c])) for c in df.columns}, indent=4)
print(out)

字符串
印刷品：

{
    "2023-06-30": {
        "Ordinary Shares Number": 7432000000.0,
        "Share Issued": 7432000000.0,
        "Net Debt": 12533000000.0,
        "Total Debt": 59965000000.0,
        "Tangible Book Value": 128971000000.0,
        "Cash Cash Equivalents And Short Term Investments": 111262000000.0,
        "Other Short Term Investments": 76558000000.0,
        "Cash And Cash Equivalents": 34704000000.0,
        "Cash Equivalents": 26226000000.0,
        "Cash Financial": 8478000000.0
    },
    "2022-06-30": {
        "Ordinary Shares Number": 7464000000.0,
        "Share Issued": 7464000000.0,
        "Net Debt": 35850000000.0,
        "Total Debt": 61270000000.0,
        "Tangible Book Value": 87720000000.0,
        "Cash Cash Equivalents And Short Term Investments": 104757000000.0,
        "Other Short Term Investments": 90826000000.0,
        "Cash And Cash Equivalents": 13931000000.0,
        "Cash Equivalents": 5673000000.0,
        "Cash Financial": 8258000000.0
    },
    "2021-06-30": {
        "Ordinary Shares Number": 7519000000.0,
        "Share Issued": 7519000000.0,
        "Net Debt": 43922000000.0,
        "Total Debt": 67775000000.0,
        "Tangible Book Value": 84477000000.0,
        "Cash Cash Equivalents And Short Term Investments": 130334000000.0,
        "Other Short Term Investments": 116110000000.0,
        "Cash And Cash Equivalents": 14224000000.0,
        "Cash Equivalents": 6952000000.0,
        "Cash Financial": 7272000000.0
    },
    "2020-06-30": {
        "Ordinary Shares Number": 7571000000.0,
        "Share Issued": 7571000000.0,
        "Net Debt": 49751000000.0,
        "Total Debt": 70998000000.0,
        "Tangible Book Value": 67915000000.0,
        "Cash Cash Equivalents And Short Term Investments": 136527000000.0,
        "Other Short Term Investments": 122951000000.0,
        "Cash And Cash Equivalents": 13576000000.0,
        "Cash Equivalents": NaN,
        "Cash Financial": NaN
    }
}

型

赞(0）分享  回复(0）举报  12个月前
zzwlnbp8

zzwlnbp82#

我们可以通过将第一列设置为DataFrame的索引，然后使用df.to_json(orient='columns')将其转换为JSON来实现相同的JSON结构。在'columns'方向上，JSON的形状是DataFrame中的每一列都成为一个键，对应的值存储在该键下的数组中。

import pandas as pd
import json

df.set_index(df.columns[0], inplace=True)
result = df.to_json(orient='columns')

parsed = json.loads(result)
print(json.dumps(parsed, indent=4))

字符串
指纹

{
    "2023-06-30": {
        "Ordinary Shares Number": 7432000000.0,
        "Share Issued": 7432000000.0,
        "Net Debt": 12533000000.0,
        "Total Debt": 59965000000.0,
        "Tangible Book Value": 129000000000.0,
        "Cash Cash Equivalents": 111000000000.0,
        "Other Short Term Investme": 76558000000.0,
        "Cash And Cash Equivalents": 34704000000.0,
        "Cash Equivalents": 26226000000.0,
        "Cash Financial": 8478000000.0
    },
    "2022-06-30": {
        "Ordinary Shares Number": 7464000000.0,
        "Share Issued": 7464000000.0,
        "Net Debt": 35850000000.0,
        "Total Debt": 61270000000.0,
        "Tangible Book Value": 87720000000.0,
        "Cash Cash Equivalents": 105000000000.0,
        "Other Short Term Investme": 90826000000.0,
        "Cash And Cash Equivalents": 13931000000.0,
        "Cash Equivalents": 5673000000.0,
        "Cash Financial": 8258000000.0
    },
    "2021-06-30": {
        "Ordinary Shares Number": 7519000000.0,
        "Share Issued": 7519000000.0,
        "Net Debt": 43922000000.0,
        "Total Debt": 67775000000.0,
        "Tangible Book Value": 84477000000.0,
        "Cash Cash Equivalents": 130000000000.0,
        "Other Short Term Investme": 116000000000.0,
        "Cash And Cash Equivalents": 14224000000.0,
        "Cash Equivalents": 6952000000.0,
        "Cash Financial": 7272000000.0
    },
    "2020-06-30": {
        "Ordinary Shares Number": 7571000000.0,
        "Share Issued": 7571000000.0,
        "Net Debt": 49751000000.0,
        "Total Debt": 70998000000.0,
        "Tangible Book Value": 67915000000.0,
        "Cash Cash Equivalents": 137000000000.0,
        "Other Short Term Investme": 123000000000.0,
        "Cash And Cash Equivalents": 13576000000.0,
        "Cash Equivalents": null,
        "Cash Financial": null
    }
}

型

赞(0）分享  回复(0）举报  12个月前
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