我是python的新手。我有一个类似以下的熊猫系列,
count
timestamp
1980-10-05 01:12:00 56.4691
1980-10-05 01:13:00 54.9415
1980-10-05 01:14:00 52.0359
1980-10-05 01:15:00 47.7313
1980-10-05 01:16:00 50.5876
1980-10-05 01:17:00 48.2846
1980-10-05 01:18:00 44.6438
1980-10-05 01:19:00 42.3077
1980-10-05 01:20:00 38.8363
1980-10-05 01:21:00 41.0145
1980-10-05 01:22:00 39.5523
1980-10-05 01:23:00 38.9117
1980-10-05 01:24:00 37.3052
1980-10-05 01:25:00 36.1725
1980-10-05 01:26:00 37.5150
1980-10-05 01:27:00 38.1387
1980-10-05 01:28:00 39.5351
1980-10-05 01:29:00 38.1834
1980-10-05 01:30:00 37.5988
1980-10-05 01:31:00 43.6522
1980-10-05 01:32:00 47.9571
1980-10-05 13:08:00 210.0000
1980-10-05 13:18:00 40.0000
1980-10-05 13:28:00 250.0000
1980-10-05 13:38:00 40.0000
我想将其转换为对象数组;
[
{timestamp: 1980-10-05 13:38:00, count: 40.0000},
{timestamp: 1980-10-05 13:38:00, count: 40.0000},
{timestamp: 1980-10-05 13:38:00, count: 40.0000},
{timestamp: 1980-10-05 13:38:00, count: 40.0000}
]
是否可以在python中完成?
答案 0 :(得分:0)
尝试这样:
arr_obj = df.to_dict(orient='records')
答案 1 :(得分:0)
您需要将$headerInfo
的{{1}}和参数reset_index
设置为:
to_json
答案 2 :(得分:0)
您可以将df.to_dict
与orient='records'
关键字一起使用。
原始数据框:
>>> df.head()
>>>
count
timestamp
1980-10-05 01:12:00 56.4691
1980-10-05 01:13:00 54.9415
1980-10-05 01:14:00 52.0359
1980-10-05 01:15:00 47.7313
1980-10-05 01:16:00 50.5876
将'timestamp'
设为一列:
>>> df = df.reset_index()
>>> df.head()
>>>
timestamp count
0 1980-10-05 01:12:00 56.4691
1 1980-10-05 01:13:00 54.9415
2 1980-10-05 01:14:00 52.0359
3 1980-10-05 01:15:00 47.7313
4 1980-10-05 01:16:00 50.5876
使用df.to_dict
获得最终结果:
>>> result = df.to_dict(orient='records')
>>> result[:5]
>>>
[{'count': 56.4691, 'timestamp': Timestamp('1980-10-05 01:12:00')},
{'count': 54.9415, 'timestamp': Timestamp('1980-10-05 01:13:00')},
{'count': 52.0359, 'timestamp': Timestamp('1980-10-05 01:14:00')},
{'count': 47.7313, 'timestamp': Timestamp('1980-10-05 01:15:00')},
{'count': 50.5876, 'timestamp': Timestamp('1980-10-05 01:16:00')}]
编辑:我不清楚您希望时间戳在最终结果中显示的精确程度。如Sandeep Kadapa's answer所示,您可能必须在to_json
上使用to_dict
。