Pivot 表用 Pandas

Question

I have the following data saved as a pandas dataframe

Animal Day age Food  kg  
 1      1   3   17   0.1 
 1      1   3   22   0.7
 1      2   3   17   0.8
 2      2   7   15   0.1

With pivot I get the following:

output = df.pivot(["Animal", "Food"], "Day", "kg") \
           .add_prefix("Day") \
           .reset_index() \
           .rename_axis(None, axis=1)

>>> output
   Animal  Food  Day1  Day2
0       1    17   0.1   0.8
1       1    22   0.7   NaN
2       2    15   NaN   0.1

However I would like to have the age column (and other columns) still included. It could also be possible that for animal x the value age is not always the same, then it doesn't matter which age value is taken.

    Animal  Food Age Day1  Day2
0       1    17   3   0.1   0.8
1       1    22   3   0.7   NaN
2       2    15   7   NaN   0.1

How do I need to change the code above?

Answer 1

IIUC, what you want is to pivot the weight, but to aggregate the age.

To my knowledge, you need to do both operations separately. One with pivot , the other with groupby (here I used first for the example, but this could be anything), and join :

(df.pivot(index=["Animal", "Food"],
          columns="Day",
          values="kg",
         )
   .add_prefix('Day')
   .join(df.groupby(['Animal', 'Food'])['age'].first())
   .reset_index()
 )

I am adding a non ambiguous example (here the age of Animal 1 changes on Day2).

Input:

   Animal  Day  age  Food   kg
0       1    1    3    17  0.1
1       1    1    3    22  0.7
2       1    2    4    17  0.8
3       2    2    7    15  0.1

output:

   Animal  Food  Day1  Day2  age
0       1    17   0.1   0.8    3
1       1    22   0.7   NaN    3
2       2    15   NaN   0.1    7

Answer 2

Use pivot , add other columns to index:

>>> df.pivot(df.columns[~df.columns.isin(['Day', 'kg'])], 'Day', 'kg') \
      .add_prefix('Day').reset_index().rename_axis(columns=None)

   Animal  age  Food  Day1  Day2
0       1    3    17   0.1   0.8
1       1    3    22   0.7   NaN
2       2    7    15   NaN   0.1