Python – An iteration of the data frame groupby

       A   B  C  
0    Bob  10  2
1    Bob  11  8
2  Sarah  23 -2
3  Sarah  24  4
4   Jack  19 -4
5   Jack  21 -1

I want to get a new df["Point"] as follows:

• For the Bob group: df["Point") is the first B value multiplied by the C value. 10*2=20； 10*8=80。

To the “

• Sarah” group: df["Point") is the first B value multiplied by the C value. 23*(-2)=(-46); 23*4=92。

To the “

• Jack” group: df["Point"] is the first B value multiplied by the C value. 19*(-4)=(-76); 19*(-1)=(-19)。

I

mean, I want to get:

       A   B  C  Point
0    Bob  10  2     20
1    Bob  11  8     80
2  Sarah  23 -2    -46
3  Sarah  24  4     92
4   Jack  19 -4    -76
5   Jack  21 -1    -19

After that, I want to do the following iteration:

results = {}

grouped = df.groupby("A")

for idx, group in grouped:
    if (group["Point"] > 50).any():
        results[idx] = group[group["Point"] > 50].head(1)
        print ("")
    else:
        results[idx] = group.tail(1)
        print ("")
    print(results[idx])

And get this result:

      A   B  C  Point
1   Bob  11  8     80

A   B  C  Point
3 Sarah  23  4     92

A   B  C  Point
5  Jack  21 -1    -19

I think I have to do

two iterations, but I don’t know how to do it or if I can do it differently.

Start by transform to create a new column with first multiplied by C column:

df['point'] = df.groupby('A')['B'].transform('first').mul(df['C'])
print (df)
       A   B  C  point
0    Bob  10  2     20
1    Bob  11  8     80
2  Sarah  23 -2    -46
3  Sarah  24  4     92
4   Jack  19 -4    -76
5   Jack  21 -1    -19

Then first filter all rows by criteria, then press > drop_ duplicates only gets the first line – keep='first' by default:

df1 = df[df['point'] > 50].drop_duplicates('A')
print (df1)
       A   B  C  point
1    Bob  11  8     80
3  Sarah  24  4     92

Then filter not in DF1. Rows and columns in A isin and inversion conditions ~ , again > drop_ duplicates only keeps the last line:

df2 = df[~df['A'].isin(df1['A'])].drop_duplicates('A', keep='last')
print (df2)
      A   B  C  point
5  Jack  21 -1    -19

Last time I used concat and dict Comprehension Final Dictionary

:

d = {k: v for k, v in pd.concat([df1, df2]).groupby('A')}
print (d)
{'Bob':      A   B  C  point
1  Bob  11  8     80, 'Jack':       A   B  C  point
5  Jack  21 -1    -19, 'Sarah':        A   B  C  point
3  Sarah  24  4     92}