使用索引和列标题将 Pandas dataframe 中的位置复制到另一个 dataframe

Question

I have two dataframes and I want to extract values from one of them and put it in a different column based on the column info and index contained in a second dataframe(my dataframe is much larger than this, so I need it generalized), df1:

        apple     banana   pear   value
127       3          7       4      0
157       5          15      26     0
241       16         14      31     0
365       42         17      18     0

df2:

        Col_Head   
127       apple     
157       pear       
365       banana 

I want df1 to look like this:

         apple     banana   pear   value
127       3          7       4      3
157       5          15      26     26
241       16         14      31     0
365       42         17      18     17

Answer 1

Convert your df2 Col_Head column to dict with index as key, Col_Head column as value.

Then apply() df1 on rows and lookup by index to get expected col head from previous dict. If index not found, use value column head.

lookup = df2.to_dict()['Col_Head']

df1['value'] = df1.apply(lambda row: row[lookup.get(row.name, 'value')], axis=1)

print(df1)

     apple  banana  pear  value
127      3       7     4      3
157      5      15    26     26
241     16      14    31      0
365     42      17    18     17

Answer 2

I have two dataframes and I want to extract values from one of them and put it in a different column based on the column info and index contained in a second dataframe(my dataframe is much larger than this, so I need it generalized), df1:

        apple     banana   pear   value
127       3          7       4      0
157       5          15      26     0
241       16         14      31     0
365       42         17      18     0

df2:

        Col_Head   
127       apple     
157       pear       
365       banana 

I want df1 to look like this:

         apple     banana   pear   value
127       3          7       4      3
157       5          15      26     26
241       16         14      31     0
365       42         17      18     17