这里我有一些附加值和日期。我想将这些数据转换成熊猫。但是我不知道该怎么做。
这是我的代码，用于读取附加值：在此代码之前，我编写了另一个代码来附加值。之后，我想将4个值读为4列。
x1.append({'date':next_record_time, 'x1':new_x1)
x2.append({'date':next_record_time, 'x2':new_x2})
x3.append({'date':next_record_time, 'x3':new_x3})
x4.append({'date':next_record_time, 'x4':new_x4})
pd.DataFrame(x1, columns=['date', 'x1'])
然后值
date x1
2018-06-08 09:15:00 500
2018-06-08 10:15:00 812.0015321149272
2018-06-08 11:15:00 1256.691444199051
2018-06-08 12:15:00 784.1793119716488
2018-06-08 13:15:00 489.3302935759677
2018-06-08 14:15:00 805.343602611898
2018-06-08 15:15:00 502.5368757877344
date x2
2018-06-08 15:00:00 2
2018-06-08 16:00:00 1.8711387084858075
2018-06-08 17:00:00 1.7505800331969679
2018-06-08 18:00:00 1.6377890312086083
2018-06-08 19:00:00 1.5322652263139487
2018-06-08 20:00:00 1.4335403883113977
2018-06-08 21:00:00 1.3411764553736159
date x3
2018-06-08 09:16:00 50
2018-06-08 10:16:00 35.357940964363564
2018-06-08 11:16:00 25.003679784788382
2018-06-08 12:16:00 17.681572674447967
2018-06-08 13:16:00 12.503680055604704
2018-06-08 14:16:00 58.842087624867226
2018-06-08 15:16:00 41.61070120919926
date x4
2018-06-08 09:15:00 40
2018-06-08 10:15:00 37.75543557660705
2018-06-08 11:15:00 35.63682288948313
2018-06-08 12:15:00 33.63709426897089
2018-06-08 13:15:00 31.749578641409713
2018-06-08 14:15:00 29.967979274504085
2018-06-08 15:15:00 28.286352771490854您可以看到这是我的四个附加值
然后，我需要将所有日期插入一列，并将x1，x2，x3，x4读为四列。我期望的输出是：
date x1 x2 x3 x4
2018-06-08 09:15:00 500 0 0 40
2018-06-08 09:16:00 0 0 50 0
2018-06-08 10:15:00 812.0015321149272 0 0 37.75543557660705
2018-06-08 10:16:00 0 0 35.357940964363564 0
2018-06-08 11:15:00 1256.691444199051 0 0 35.63682288948313
2018-06-08 11:16:00 0 0 25.003679784788382 0
2018-06-08 12:15:00 784.1793119716488 0 0 33.63709426897089
2018-06-08 12:16:00 0 0 17.681572674447967 0
2018-06-08 13:15:00 489.3302935759677 0 0 31.74957864140971
2018-06-08 13:16:00 0 0 12.503680055604704 0
2018-06-08 14:15:00 805.343602611898 0 0 29.967979274504085
2018-06-08 14:16:00 0 0 58.842087624867226 0
2018-06-08 15:00:00 0 2 0 0
2018-06-08 15:15:00 502.5368757877344 0 0 28.286352771490854
2018-06-08 15:16:00 0 0 1.8711387084858075 0 转换为熊猫后，我只有一个数据值。这是我得到的：
date x1
0 2018-06-08 09:15:00 40.000000
1 2018-06-08 10:15:00 37.755436
2 2018-06-08 11:15:00 35.636823
3 2018-06-08 12:15:00 33.637094
4 2018-06-08 13:15:00 31.749579
5 2018-06-08 14:15:00 29.967979有人可以帮我解决这个问题吗？
IIUC，
[首先，将所有词典传递到列表中，然后我们将它们作为一个数据帧读取，然后我们可以将其合并并堆叠以获取所需的结果。
data_dicts = x1 + x2 + x3 + x4
df = pd.DataFrame.from_dict(data_dicts)
df['date'] = df['date'].apply(pd.to_datetime)
s = pd.melt(df,id_vars='date')
final = s.groupby(['date','variable'])['value'].first().unstack().reset_index().fillna(0)
del final.columns.name
print(final)
date x1 x2 x3 x4
0 2018-06-08 09:15:00 500.000000 0.000000 0.000000 40.000000
1 2018-06-08 09:16:00 0.000000 0.000000 50.000000 0.000000
2 2018-06-08 10:15:00 812.001532 0.000000 0.000000 37.755436
3 2018-06-08 10:16:00 0.000000 0.000000 35.357941 0.000000
4 2018-06-08 11:15:00 1256.691444 0.000000 0.000000 35.636823
5 2018-06-08 11:16:00 0.000000 0.000000 25.003680 0.000000
6 2018-06-08 12:15:00 784.179312 0.000000 0.000000 33.637094
7 2018-06-08 12:16:00 0.000000 0.000000 17.681573 0.000000
8 2018-06-08 13:15:00 489.330294 0.000000 0.000000 31.749579
9 2018-06-08 13:16:00 0.000000 0.000000 12.503680 0.000000
10 2018-06-08 14:15:00 805.343603 0.000000 0.000000 29.967979
11 2018-06-08 14:16:00 0.000000 0.000000 58.842088 0.000000
12 2018-06-08 15:00:00 0.000000 2.000000 0.000000 0.000000
13 2018-06-08 15:15:00 502.536876 0.000000 0.000000 28.286353
14 2018-06-08 15:16:00 0.000000 0.000000 41.610701 0.000000
15 2018-06-08 16:00:00 0.000000 1.871139 0.000000 0.000000
16 2018-06-08 17:00:00 0.000000 1.750580 0.000000 0.000000
17 2018-06-08 18:00:00 0.000000 1.637789 0.000000 0.000000
18 2018-06-08 19:00:00 0.000000 1.532265 0.000000 0.000000
19 2018-06-08 20:00:00 0.000000 1.433540 0.000000 0.000000
20 2018-06-08 21:00:00 0.000000 1.341176 0.000000 0.000000