我在pandas df中有如下数据：
Date current_temperature
2020-06-29 14:04:21.000000 60.06
2020-06-29 14:19:26.000000 61.47
2020-06-29 14:34:30.000000 62.15
2020-06-29 14:49:35.000000 62.65
2020-06-29 15:04:39.000000 62.20
2020-06-29 15:19:44.000000 62.51
2020-06-29 15:34:48.000000 63.09
2020-06-29 15:49:53.000000 63.19
2020-06-29 16:04:58.000000 64.17
2020-06-29 16:20:02.000000 64.40
2020-06-29 16:35:07.000000 64.78
2020-06-29 16:50:11.000000 63.30
2020-06-29 17:05:16.000000 63.27
2020-06-29 17:20:20.000000 63.88
2020-06-29 17:35:25.000000 65.82
2020-06-29 17:50:30.000000 66.15
2020-06-29 18:05:34.000000 66.20
2020-06-29 18:20:39.000000 66.00
我如何将其转换为这样的json格式？：
chart.data = [{
"Date": "2012-07-27",
"current_temperature": 13
}, {
"Date": "2012-07-28",
"current_temperature": 11
}, {
"Date": "2012-07-29",
"current_temperature": 15
}, {
"Date": "2012-07-30",
"current_temperature": 16
}, {
"Date": "2012-07-31",
"current_temperature": 18
}]
如果我使用data = df.to_json()此创建：
'{"Date":{"0":"2020-06-29 14:04:21.000000","1":"2020-06-29 14:19:26.000000","2":"2020-06-29 14:34:30.000000","3":"2020-06-29 14:49:35.000000","4":"2020-06-29 15:04:39.000000","5":"2020-06-29 15:19:44.000000","6":"2020-06-29 15:34:48.000000","7":"2020-06-29 15:49:53.000000","8":"2020-06-29 16:04:58.000000","9":"2020-06-29 16:20:02.000000","10":"2020-06-29 16:35:07.000000","11":"2020-06-29 16:50:11.000000","12":"2020-06-29 17:05:16.000000","13":"2020-06-29 17:20:20.000000","14":"2020-06-29 17:35:25.000000","15":"2020-06-29 17:50:30.000000","16":"2020-06-29 18:05:34.000000","17":"2020-06-29 18:20:39.000000"},"current_temperature":{"0":60.06,"1":61.47,"2":62.15,"3":62.65,"4":62.2,"5":62.51,"6":63.09,"7":63.19,"8":64.17,"9":64.4,"10":64.78,"11":63.3,"12":63.27,"13":63.88,"14":65.82,"15":66.15,"16":66.2,"17":66.0}}'
任何提示，不胜感激...它几乎就像要去javascipt表示法，我需要每个pd数据帧行的嵌套字典？？？
编辑
如果我用 df3 = df2.to_dict(orient='index')
它被关闭，但是这是json格式吗？最后，我正在尝试找出一种方法，将数据从Flask应用程序传递到Web开发的前端，并使用JavaScript创建图表。（仍在这里学习...）
{0: {'Date': '2020-06-29 14:04:21.000000', 'current_temperature': 60.06},
1: {'Date': '2020-06-29 14:19:26.000000', 'current_temperature': 61.47},
2: {'Date': '2020-06-29 14:34:30.000000', 'current_temperature': 62.15},
3: {'Date': '2020-06-29 14:49:35.000000', 'current_temperature': 62.65},
4: {'Date': '2020-06-29 15:04:39.000000', 'current_temperature': 62.2},
5: {'Date': '2020-06-29 15:19:44.000000', 'current_temperature': 62.51},
6: {'Date': '2020-06-29 15:34:48.000000', 'current_temperature': 63.09},
7: {'Date': '2020-06-29 15:49:53.000000', 'current_temperature': 63.19},
8: {'Date': '2020-06-29 16:04:58.000000', 'current_temperature': 64.17},
9: {'Date': '2020-06-29 16:20:02.000000', 'current_temperature': 64.4},
10: {'Date': '2020-06-29 16:35:07.000000', 'current_temperature': 64.78},
11: {'Date': '2020-06-29 16:50:11.000000', 'current_temperature': 63.3},
12: {'Date': '2020-06-29 17:05:16.000000', 'current_temperature': 63.27},
13: {'Date': '2020-06-29 17:20:20.000000', 'current_temperature': 63.88},
14: {'Date': '2020-06-29 17:35:25.000000', 'current_temperature': 65.82},
15: {'Date': '2020-06-29 17:50:30.000000', 'current_temperature': 66.15},
16: {'Date': '2020-06-29 18:05:34.000000', 'current_temperature': 66.2},
17: {'Date': '2020-06-29 18:20:39.000000', 'current_temperature': 66.0}}
to_json()返回一个字符串ast.literal_eval可将字符串转换为对象Date格式化为2012-07-31，则可以在使用之前对其进行格式化to_jsonfrom ast import literal_eval
data = literal_eval(df.to_json(orient='records'))
print(data)
[{'Date': '2020-06-29 14:04:21.000000', 'current_temperature': 60.06},
{'Date': '2020-06-29 14:19:26.000000', 'current_temperature': 61.47},
{'Date': '2020-06-29 14:34:30.000000', 'current_temperature': 62.15},
{'Date': '2020-06-29 14:49:35.000000', 'current_temperature': 62.65},
{'Date': '2020-06-29 15:04:39.000000', 'current_temperature': 62.2},
{'Date': '2020-06-29 15:19:44.000000', 'current_temperature': 62.51},
{'Date': '2020-06-29 15:34:48.000000', 'current_temperature': 63.09},
{'Date': '2020-06-29 15:49:53.000000', 'current_temperature': 63.19},
{'Date': '2020-06-29 16:04:58.000000', 'current_temperature': 64.17},
{'Date': '2020-06-29 16:20:02.000000', 'current_temperature': 64.4},
{'Date': '12020-06-29 16:35:07.000000', 'current_temperature': 64.78},
{'Date': '2020-06-29 16:50:11.000000', 'current_temperature': 63.3},
{'Date': '2020-06-29 17:05:16.000000', 'current_temperature': 63.27},
{'Date': '2020-06-29 17:20:20.000000', 'current_temperature': 63.88},
{'Date': '2020-06-29 17:35:25.000000', 'current_temperature': 65.82},
{'Date': '2020-06-29 17:50:30.000000', 'current_temperature': 66.15},
{'Date': '2020-06-29 18:05:34.000000', 'current_temperature': 66.2},
{'Date': '2020-06-29 18:20:39.000000', 'current_temperature': 66.0}]
dd = dict(dd)摆脱结果的defaultdict和class部分。from collection import defaultdict
# using data from above
dd = defaultdict(list)
for d in data:
for k, v in d.items():
dd[k].append(v)
# print(dd)
defaultdict(<class 'list'>,
{'Date': ['2020-06-29 14:04:21.000000',
'2020-06-29 14:19:26.000000',
'2020-06-29 14:34:30.000000',
'2020-06-29 14:49:35.000000',
'2020-06-29 15:04:39.000000',
'2020-06-29 15:19:44.000000',
'2020-06-29 15:34:48.000000',
'2020-06-29 15:49:53.000000',
'2020-06-29 16:04:58.000000',
'2020-06-29 16:20:02.000000',
'12020-06-29 16:35:07.000000',
'2020-06-29 16:50:11.000000',
'2020-06-29 17:05:16.000000',
'2020-06-29 17:20:20.000000',
'2020-06-29 17:35:25.000000',
'2020-06-29 17:50:30.000000',
'2020-06-29 18:05:34.000000',
'2020-06-29 18:20:39.000000'],
'current_temperature': [60.06,
61.47,
62.15,
62.65,
62.2,
62.51,
63.09,
63.19,
64.17,
64.4,
64.78,
63.3,
63.27,
63.88,
65.82,
66.15,
66.2,
66.0]})
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我来说两句