Is there a simple way in Pyspark to find out number of promotions it took to convert someone into customer? - pyspark

Question

I have a date-level promotion data frame that looks something like this:
ID
Date
Promotions
Converted to customer
1
2-Jan
2
0
1
10-Jan
3
1
1
14-Jan
3
0
2
10-Jan
19
1
2
10-Jan
8
0
2
10-Jan
12
0
Now I want to see what were the number of promotions it took to convert someone into a customer
For eg., It took (2+3) promotions to convert ID 1 to the customer and (19) to convert ID 2 to the customer.
Eg.
ID
Date
1
5
2
19
I am unable to think of an idea to solve it. Can you please help me?
#Corralien and mozway have helped with the solution in Python. But I am unable to implement it in Pyspark because of the huge dataframe size (>1 TB).

Answer 1

You can use:
prom = (df.groupby('ID')['Promotions'].cumsum()
.where(df['Converted to customer'].eq(1))
.dropna().astype(int))
out = df.loc[prom.index, ['ID', 'Date']].assign(Promotion=prom)
print(out)
# Output
ID Date Promotion
1 1 10-Jan 5
3 2 10-Jan 19

Answer 2

Use one groupby to generate a mask to hide the rows, then one groupby.sum for the sum:
mask = (df.groupby('ID', group_keys=False)['Converted to customer']
.apply(lambda s: s.eq(1).shift(fill_value=False).cummax())
)
out = df[~mask].groupby('ID')['Promotions'].sum()
Output:
ID
1 5
2 19
Name: Promotions, dtype: int64
Alternative output:
df[~mask].groupby('ID', as_index=False).agg(**{'Number': ('Promotions', 'sum')})
Output:
ID Number
0 1 5
1 2 19
If you potentially have groups without conversion to customer, you might want to also aggregate the "" column as indicator:
mask = (df.groupby('ID', group_keys=False)['Converted to customer']
.apply(lambda s: s.eq(1).shift(fill_value=False).cummax())
)
out = (df[~mask]
.groupby('ID', as_index=False)
.agg(**{'Number': ('Promotions', 'sum'),
'Converted': ('Converted to customer', 'max')
})
)
Output:
ID Number Converted
0 1 5 1
1 2 19 1
2 3 39 0
Alternative input:
ID Date Promotions Converted to customer
0 1 2-Jan 2 0
1 1 10-Jan 3 1
2 1 14-Jan 3 0
3 2 10-Jan 19 1
4 2 10-Jan 8 0
5 2 10-Jan 12 0
6 3 10-Jan 19 0 # this group has
7 3 10-Jan 8 0 # no conversion
8 3 10-Jan 12 0 # to customer

Answer 3

you want to compute something by ID, so a groupby ID seems appropriate, e.g.
data.groupby("ID").apply(fct)
Now write a separate function agg_fct which computes the result for a
dataframe consisting of only one ID
Assuming data are ordered by Date, I guess that
def agg_fct(df):
index_of_conv = df["Converted to customer"].argmax()
return df.iloc[0:index_of_conv,df.columns.get_loc("Promotions")].sum()
would be fine. You might want to make some adjustments in case of a customer who has never been converted.