如何将选择列值的熊猫行折叠为最小组合并映射回原始行

Question

上下文：

我有一个包含 7 列（味道、颜色、温度、纹理、形状、age_of_participant、name_of_participant）的 pandas 数据框。

在 7 列中，味道、颜色、温度、质地和形状可以在多行中有重叠的值（即味道可能不止一行是酸的）

我正在尝试将所有行折叠成给定的最少数量的组合 味道、颜色、温度、纹理和形状值，而忽略 NA（换句话说，覆盖它们）。下一部分是将这些行中的每一行映射到原始行。

模拟数据集：

data_set = [
   {'color':'brown', 'age_of_participant':23, 'name_of_participant':'feb'},
   {'taste': 'sour', 'color':'green', 'temperature': 'hot', 'age_of_participant':16,'name_of_participant': 'joe'},
   {'taste': 'sour', 'color':'green', 'texture':'soft', 'age_of_participant':17,'name_of_participant': 'jane'},
   {'color':'green','age_of_participant':18,'name_of_participant': 'jeff'},
   {'taste': 'sweet', 'color':'red', 'age_of_participant':19,'name_of_participant': 'joke'},
   {'taste': 'sweet', 'temperature': 'cold',  'age_of_participant':20,'name_of_participant': 'jolly'},
   {'taste': 'salty', 'color':'purple', 'texture':'soft', 'age_of_participant':21,'name_of_participant': 'jupyter'},
   {'taste': 'salty', 'color':'brown', 'age_of_participant':22,'name_of_participant': 'january'}

]

import pandas as pd
import random

data_set = random.sample(data_set, k=len(data_set))

data_frame = pd.DataFrame(data_set)

print(data_frame)

   age_of_participant   color name_of_participant  taste temperature texture
0                  16   green                 joe   sour         hot     NaN
1                  17   green                jane   sour         NaN    soft
2                  18   green                jeff    NaN         NaN     NaN
3                  19     red                joke  sweet         NaN     NaN
4                  20     NaN               jolly  sweet        cold     NaN
5                  21  purple             jupyter  salty         NaN    soft
6                  22   brown             january  salty         NaN     NaN

我的尝试：

# These columns are used to do the grouping since age_of_participant and name_of_participant are unique per row

values_that_can_be_grouped = ['taste', 'color','temperature','texture']

sub_set = data_frame[values_that_can_be_grouped].drop_duplicates().reset_index(drop=False)

my_unique_set = sub_set.groupby('taste', as_index=False).first()

print(my_unique_set)


   taste  index  color temperature texture
0             2  green                    
1  salty      6  brown                    
2   sour      1  green                soft
3  sweet      4               cold        

此时我不太确定如何将上面的行映射到除索引 2、6、1、4 之外的所有原始行。我检查了pandas code，看起来其他索引没有保存在任何地方？

我想要达到的目标：

   age_of_participant   color name_of_participant  taste temperature texture
0                  16   green                 joe   sour         hot    soft
1                  17   green                jane   sour         hot    soft
2                  18   green                jeff   sour         hot    soft
3                  19     red                joke  sweet        cold     NaN
4                  20     red               jolly  sweet        cold     NaN
5                  21  purple             jupyter  salty         NaN    soft
6                  22   brown             january  salty         NaN     NaN

Answer 1

data_frame.assign(color=data_frame.color.ffill()).groupby('color').apply(lambda x: x.ffill().bfill())
Out[1089]: 
   age_of_participant   color name_of_participant  taste temperature texture
0                  16   green                 joe   sour         hot    soft
1                  17   green                jane   sour         hot    soft
2                  18   green                jeff   sour         hot    soft
3                  19     red                joke  sweet        cold     NaN
4                  20     red               jolly  sweet        cold     NaN
5                  21  purple             jupyter  salty         NaN    soft
6                  22   brown             january  salty         NaN     NaN

Answer 2

IIUC 我觉得每个taste 和color 都使用ffill 和bfill，然后groupby 他们在这里更安全

df.taste.fillna(df.groupby('color').taste.apply(lambda x : x.ffill().bfill()),inplace=True)
df.color.fillna(df.groupby('taste').color.apply(lambda x : x.ffill().bfill()),inplace=True)
df=df.groupby(['color','taste']).apply(lambda x : x.ffill().bfill())
df
   age_of_participant   color   ...   temperature texture
0                  16   green   ...           hot    soft
1                  17   green   ...           hot    soft
2                  18   green   ...           hot    soft
3                  19     red   ...          cold     NaN
4                  20     red   ...          cold     NaN
5                  21  purple   ...           NaN    soft
6                  22   brown   ...           NaN     NaN
[7 rows x 6 columns]