将数据 table 转置为时间序列 table

Question

我想知道如何将我的数据（1 行 = 参数）转置为时间序列（1 行 = 1 DateTime）我从 pandas 尝试了 pivot_table 但是...输出中没有列

我希望按日期时间（索引）对值进行分组，然后为每个标记名分组 1 列，以便将值设为 Table 值

#df = my sample of data
df = pd.DataFrame(data= csv, columns = ['DateTime','TagName','Value'])
df.pivot_table(index='DateTime',columns='TagName',values='Value',aggfunc=np.mean)

原始数据：

1

我的 pivot_table 输出：

2

感谢您的帮助。

我的数据样本：

{'DateTime': {0: '2021-10-23 10:14:29.7270000',
 1: '2021-10-23 10:14:29.7270000',
 2: '2021-10-23 10:14:29.7270000',
 3: '2021-10-23 10:14:29.7270000',
 4: '2021-10-23 10:14:29.7270000',
 5: '2021-10-23 10:14:29.7270000',
 6: '2021-10-23 10:14:29.7270000',
 7: '2021-10-23 10:14:29.7270000',
 8: '2021-10-23 10:14:29.7270000',
 9: '2021-10-23 10:14:29.7270000'},
'TagName': {0: 'DepollutionEntree.ChemineeOuvert',
 1: 'DepollutionEntree.ConsigneDepol',
 2: 'DepollutionEntree.TempForming',
 3: 'DepollutionSortie.ChemineeOuvert',
 4: 'DepollutionSortie.ConsigneDepol',
 5: 'DepollutionSortie.TempForming',
 6: 'Etuve.DebitGaz',
 7: 'FibrageB1_DebitEauDilution.PV',
 8: 'FibrageB2_DebitEauDilution.PV',
 9: 'FibrageB3_DebitEauDilution.PV'},
'Value': {0: '0',
 1: '45',
 2: '59',
 3: '0',
 4: '66',
 5: '62',
 6: '6492604',
 7: '920.399963378906',
 8: '920.039978027344',
 9: '912'}}

Answer 1

试试 pivot:

output = df.pivot("DateTime", "TagName", "Value")

>>> output 
TagName                     DepollutionEntree.ChemineeOuvert  ... FibrageB3_DebitEauDilution.PV
DateTime                                                      ...                              
2021-10-23 10:14:29.7270000                                0  ...                           912

[1 rows x 10 columns]

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