Pandas: groupby and make a new column applying aggregate to two columns

Question

I\'m having a difficulty with applying agg to a groupby pandas dataframe.

I have a dataframe df like this:

order_i         


        

Answer 1

Using groupby with first:

s = df.groupby('order_id').transform('first')
df.assign(crow=s.distance_theo.div(s.bird_distance))

   order_id  distance_theo  bird_distance      crow
0        10            100             80  1.250000
1        10             80             80  1.250000
2        10             70             80  1.250000
3        11             90             70  1.285714
4        11             70             70  1.285714
5        11             60             70  1.285714
6        12            200            180  1.111111
7        12            150            180  1.111111
8        12            100            180  1.111111
9        12             60            180  1.111111

Answer 2

You could do it without groupby and use drop_duplicate and join:

df.join(df.drop_duplicates('order_id')\
  .eval('crow = distance_theo / bird_distance')[['crow']]).ffill()

or use assign instead of eval per @jezraela comments below:

df1.join(df1.drop_duplicates('order_id')\
   .assign(crow=df1.distance_theo / df1.bird_distance)[['crow']]).ffill()

Output:

   order_id  distance_theo  bird_distance      crow
0        10            100             80  1.250000
1        10             80             80  1.250000
2        10             70             80  1.250000
3        11             90             70  1.285714
4        11             70             70  1.285714
5        11             60             70  1.285714
6        12            200            180  1.111111
7        12            150            180  1.111111
8        12            100            180  1.111111
9        12             60            180  1.111111