Question

我在熊猫中有以下数据框

  date        prod    hourly_bucket      tank      trans      flag     
  01-01-2019  TP      05:00:00-06:00:00  2         Preset     Peak
  01-01-2019  TP      05:00:00-06:00:00  2         Preset     Peak
  01-01-2019  TP      05:00:00-06:00:00  2         Non Preset Peak
  02-01-2019  TP      05:00:00-06:00:00  2         Preset     Lean
  02-01-2019  TP      05:00:00-06:00:00  2         Preset     Lean
  02-01-2019  TP      05:00:00-06:00:00  2         Non Preset Lean

我想要的数据帧将是日级别和槽级别的聚合，然后计算Preset,Non-Preset小时内有多少Lean and Peak个交易

  date       tank   Lean_Non_Preset  Lean_Preset  Peak_Non_Preset  Peak_Preset
  01-01-2019 2      1                2            1                2

我正在熊猫后面追随

 lean_peak_preset_cnt = df.pivot_table(index=['date','tank'], columns=['flag'],values=['trans'],aggfunc='count').reset_index()

但是它没有给我所需的解决方案

Answer 1

将'trans'添加到参数columns，然后在MultiIndex和map的列中展平join：

lean_peak_preset_cnt = df.pivot_table(index=['date','tank'], 
                                      columns=['flag','trans'],
                                      aggfunc='size', 
                                      fill_value=0) 

lean_peak_preset_cnt.columns = lean_peak_preset_cnt.columns.map('_'.join)
lean_peak_preset_cnt = lean_peak_preset_cnt.reset_index() 
print (lean_peak_preset_cnt)

         date  tank  Lean_No Preset  Lean_Preset  Peak_Non Preset  Peak_Preset
0  01-01-2019     2               0            0                1            2
1  02-01-2019     2               1            2                0            0

Answer 2

您快到了：

piv = (df.pivot_table(index=['date', 'tank'], columns=['trans', 'flag'], 
                      aggfunc='size', fill_value=0))
piv.columns = piv.columns.ravel()

size函数提供所需的计数，您希望将非计数值填充为0，并指定所需的列和索引。有关更多详细信息，请参见docs。 ravel将您的多索引列合并到一个级别。

                 (Nonpreset, Lean)  (Nonpreset, Peak)  (Preset, Lean)  \
#date       tank                                                         
#01-01-2019 2                     0                  1               0   
#02-01-2019 2                     1                  0               2   

                 (Preset, Peak)  
#date       tank                  
#01-01-2019 2                  2  
#02-01-2019 2                  0

带有多列的熊猫数据透视表

2 个答案: