使用 pandas - 单行输出将数据帧从长转换为宽

Question

我有一个如下所示的数据框

df = pd.DataFrame({
'subject_id':[1,1,1,1,2,2,2,2],
'date':['2173/04/11','2173/04/12','2173/04/11','2173/04/12','2173/05/14','2173/05/15','2173/05/14','2173/05/15'],
'time_1':['2173/04/11 12:35:00','2173/04/12 12:50:00','2173/04/11 12:59:00','2173/04/12 13:14:00','2173/05/14 13:37:00','2173/05/15 13:39:00','2173/05/14 18:37:00','2173/05/15 19:39:00'],
 'val' :[5,5,40,40,7,7,38,38],
 'iid' :[12,12,12,12,21,21,21,21]   
 })
df['time_1'] = pd.to_datetime(df['time_1'])
df['day'] = df['time_1'].dt.day

我尝试使用 stack,unstack,pivot and melt 方法，但似乎没有帮助

pd.melt(df, id_vars =['subject_id','val'], value_vars =['date','val']) #1

df.unstack().reset_index()  #2

df.pivot(index='subject_id', columns='time_1', values='val')  #3

我希望我的输出数据框如下所示

更新截图

Answer 1

想法是由 GroupBy.cumcount with same column/columns for new index - here subject_id, create MultiIndex, reshape by DataFrame.unstack 创建助手系列并最后展平 MulitIndex in columns:

cols = ['time_1','val']
df = df.set_index(['subject_id', df.groupby('subject_id').cumcount().add(1)])[cols].unstack()
df.columns = [f'{a}{b}' for a, b in df.columns]
df = df.reset_index()
print (df)
   subject_id             time_11             time_12             time_13  \
0           1 2173-04-11 12:35:00 2173-04-12 12:50:00 2173-04-11 12:59:00   
1           2 2173-05-14 13:37:00 2173-05-15 13:39:00 2173-05-14 18:37:00   

              time_14  val1  val2  val3  val4  
0 2173-04-12 13:14:00     5     5    40    40  
1 2173-05-15 19:39:00     7     7    38    38

如果 id 组的数量不同，则需要缺失值 - unstack 使用最大计数，然后添加缺失值：

df = pd.DataFrame({
'subject_id':[1,1,1,2,2,3],
'date':['2173/04/11','2173/04/12','2173/04/11','2173/04/12','2173/05/14','2173/05/15'],
'time_1':['2173/04/11 12:35:00','2173/04/12 12:50:00','2173/04/11 12:59:00',
          '2173/04/12 13:14:00','2173/05/14 13:37:00','2173/05/15 13:39:00'],
 'val' :[5,5,40,40,7,7],
 'iid' :[12,12,12,12,21,21]   
 })
df['time_1'] = pd.to_datetime(df['time_1'])
df['day'] = df['time_1'].dt.day
print (df)
   subject_id        date              time_1  val  iid  day
0           1  2173/04/11 2173-04-11 12:35:00    5   12   11
1           1  2173/04/12 2173-04-12 12:50:00    5   12   12
2           1  2173/04/11 2173-04-11 12:59:00   40   12   11
3           2  2173/04/12 2173-04-12 13:14:00   40   12   12
4           2  2173/05/14 2173-05-14 13:37:00    7   21   14
5           3  2173/05/15 2173-05-15 13:39:00    7   21   15

cols = ['time_1','val']
df = df.set_index(['subject_id', df.groupby('subject_id').cumcount().add(1)])[cols].unstack()
df.columns = [f'{a}{b}' for a, b in df.columns]
df = df.reset_index()
print (df)
   subject_id             time_11             time_12             time_13  \
0           1 2173-04-11 12:35:00 2173-04-12 12:50:00 2173-04-11 12:59:00   
1           2 2173-04-12 13:14:00 2173-05-14 13:37:00                 NaT   
2           3 2173-05-15 13:39:00                 NaT                 NaT   

   val1  val2  val3  
0   5.0   5.0  40.0  
1  40.0   7.0   NaN  
2   7.0   NaN   NaN

使用 pandas - 单行输出将数据帧从长转换为宽

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python

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dataframe

python-3.x

pandas