pandas calculate scores for each group based on multiple functions - python-3.x

Question

I have the following df,
group_id code amount date
1 100 20 2017-10-01
1 100 25 2017-10-02
1 100 40 2017-10-03
1 100 25 2017-10-03
2 101 5 2017-11-01
2 102 15 2017-10-15
2 103 20 2017-11-05
I like to groupby group_id and then compute scores to each group based on the following features:
if code values are all the same in a group, score 0 and 10 otherwise;
if amount sum is > 100, score 20 and 0 otherwise;
sort_values by date in descending order and sum the differences between the dates, if the sum < 5, score 30, otherwise 0.
so the result df looks like,
group_id code amount date score
1 100 20 2017-10-01 50
1 100 25 2017-10-02 50
1 100 40 2017-10-03 50
1 100 25 2017-10-03 50
2 101 5 2017-11-01 10
2 102 15 2017-10-15 10
2 103 20 2017-11-05 10
here are the functions that correspond to each feature above:
def amount_score(df, amount_col, thold=100):
if df[amount_col].sum() > thold:
return 20
else:
return 0
def col_uniq_score(df, col_name):
if df[col_name].nunique() == 1:
return 0
else:
return 10
def date_diff_score(df, col_name):
df.sort_values(by=[col_name], ascending=False, inplace=True)
if df[col_name].diff().dropna().sum() / np.timedelta64(1, 'D') < 5:
return score + 30
else:
return score
I am wondering how to apply these functions to each group and calculate the sum of all the functions to give a score.

Answer 1

You can try groupby.transform for same size of Series as original DataFrame with numpy.where for if-else for Series:
grouped = df.sort_values('date', ascending=False).groupby('group_id', sort=False)
a = np.where(grouped['code'].transform('nunique') == 1, 0, 10)
print (a)
[10 10 10 0 0 0 0]
b = np.where(grouped['amount'].transform('sum') > 100, 20, 0)
print (b)
[ 0 0 0 20 20 20 20]
c = np.where(grouped['date'].transform(lambda x:x.diff().dropna().sum()).dt.days < 5, 30, 0)
print (c)
[30 30 30 30 30 30 30]
df['score'] = a + b + c
print (df)
group_id code amount date score
0 1 100 20 2017-10-01 40
1 1 100 25 2017-10-02 40
2 1 100 40 2017-10-03 40
3 1 100 25 2017-10-03 50
4 2 101 5 2017-11-01 50
5 2 102 15 2017-10-15 50
6 2 103 20 2017-11-05 50