Question

我正在寻找有关熊猫DataFrame排序的帮助。我有一个8列的数据框。

['Date' , 'S ID', 'Se ID', 'S #', 'File Size (Mb)', 'HD name', 'Start Time', 'End time']

然后，我做了：

DataFile.groupby(['HD Name','Date','Se ID','S ID'])['File Size (Mb)'].agg({'Sequence #':'count','File Size (Mb)':'sum'}).reset_index().rename(columns={'Sequence #':'# of Files'})

将获取我的数据，并通过匹配的grouby（）参数对其进行分组，并对文件大小求和。我想在上面添加两列，其中包含第一个“开始时间”和最后一个“结束时间”，我将如何去做呢？
我在想，我唯一的选择可能是遍历数据或创建重复的数据框，以查看分组数据的开始和结束时间。
任何想法将不胜感激！

示例数据框：

“硬盘名称”：[H5，H5，H5，H5，H5]
  'S ID'：[LA，LA，LA，SD，SD]
  'Se ID'：[1200，1200，1200，30，30]
  '日期'：['10 / 01/2018'，'10 / 01/2018'，'10 / 01/2018'，'09 / 03/2018'，'09 / 03/2018']
  '＃'：[1、2、3、1、2]
  “开始时间”：[[08:09:54]，[08:58:31]，[09:39:38]，[05:04:13]，[05:41:13]]
  “结束时间”：[[08:28:54]，[09:17:31]，[09:58:38]，[05:23:12]，[06:00:12]]

 {'Date': {34: '10/01/2018',
35: '10/01/2018',
36: '10/01/2018',
37: '10/01/2018',
38: '10/01/2018',
39: '10/01/2018',
40: '10/01/2018',
41: '10/01/2018',
42: '10/01/2018',
661: '09/03/2018'},  

'End Time': {34: ['08:28:54'],
35: ['09:17:31'],
36: ['09:58:38'],
37: ['10:37:41'],
38: ['11:21:32'],
39: ['12:04:42'],
40: ['12:45:31'],
41: ['13:25:23'],
42: ['14:04:03'],
661: ['05:53:36']},  

'File Size (Mb)': {34: 1074.256284,
35: 1074.842244,
36: 1074.759444,
37: 1074.836956,
38: 1074.516156,
39: 1074.547044,
40: 1074.8363,
41: 1074.891492,
42: 1074.792068,
661: 1074.428204},  

'Hard Drive Name': {34: 'H5',
35: 'H5',
36: 'H5',
37: 'H5',
38: 'H5',
39: 'H5',
40: 'H5',
41: 'H5',
42: 'H5',
661: 'H5'},  

'Sensor ID': {34: '1207',
35: '1207',
36: '1207',
37: '1207',
38: '1207',
39: '1207',
40: '1207',
41: '1207',
42: '1207',
661: '1207'},  

'Sequence #': {34: 's005',
35: 's006',
36: 's007',
37: 's008',
38: 's009',
39: 's010',
40: 's011',
41: 's012',
42: 's013',
661: 's000'},  

'Site ID': {34: 'SD',
35: 'SD',
36: 'SD',
37: 'SD',
38: 'SD',
39: 'SD',
40: 'SD',
41: 'SD',
42: 'SD',
661: 'SDO'},  

'Start Time': {34: ['08:09:54'],
35: ['08:58:31'],
36: ['09:39:38'],
37: ['10:18:41'],
38: ['11:02:32'],
39: ['11:45:42'],
40: ['12:26:31'],
41: ['13:06:23'],
42: ['13:45:03'],
661: ['05:34:37']}}

Answer 1

好的，您需要将pd.to_timedelta与.str访问器一起使用：

其中d等于df.head（10）.to_dict（）输出：

df = pd.DataFrame(d)

df['Start Time'] = pd.to_timedelta(df['Start Time'].str[0])
df['End Time']  = pd.to_timedelta(df['End Time'].str[0])

df_out = df.groupby(['Hard Drive Name','Date','Sensor ID','Site ID'])['Sequence #',
                                                                      'File Size (Mb)',
                                                                      'Start Time',
                                                                      'End Time']\
           .agg({'Sequence #':'count',
                 'File Size (Mb)':'sum',
                 'Start Time':'min',
                 'End Time':'max'})\
           .reset_index()\
           .rename(columns={'Sequence #':'# of Files'})

输出：

  Hard Drive Name        Date Sensor ID Site ID  # of Files  File Size (Mb) Start Time End Time
0              H5  09/03/2018      1207     SDO           1     1074.428204   05:34:37 05:53:36
1              H5  10/01/2018      1207      SD           9     9672.277988   08:09:54 14:04:03

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