Year wise aggregation on the given condition in pandas - pandas

Question

I have a data frame as shown below. which is a sales data of two health care product starting from December 2016 to November 2018.
product price sale_date discount
A 50 2016-12-01 5
A 50 2017-01-03 4
B 200 2016-12-24 10
A 50 2017-01-18 3
B 200 2017-01-28 15
A 50 2017-01-18 6
B 200 2017-01-28 20
A 50 2017-04-18 6
B 200 2017-12-08 25
A 50 2017-11-18 6
B 200 2017-08-21 20
B 200 2017-12-28 30
A 50 2018-03-18 10
B 300 2018-06-08 45
B 300 2018-09-20 50
A 50 2018-11-18 8
B 300 2018-11-28 35
From the above I would like to prepare below data frame
Expected Output:
product year number_of_months total_price total_discount number_of_sales
A 2016 1 50 5 1
B 2016 1 200 10 1
A 2017 12 250 25 5
B 2017 12 1000 110 5
A 2018 11 100 18 2
B 2018 11 900 130 3
Note: Please note that the data starts from Dec 2016 to Nov 2018.
So number of months in 2016 is 1, in 2017 we have full data so 12 months and 2018 we have 11 months.

Answer 1

First aggregate sum by years and product and then create new column for counts by months by DataFrame.insert and Series.map:
df1 =(df.groupby(['product',df['sale_date'].dt.year], sort=False).sum().add_prefix('total_')
.reset_index())
df1.insert(2,'number_of_months', df1['sale_date'].map({2016:1, 2017:12, 2018:11}))
print (df1)
product sale_date number_of_months total_price total_discount
0 A 2016 1 50 5
1 A 2017 12 250 25
2 B 2016 1 200 10
3 B 2017 12 1000 110
4 A 2018 11 100 18
5 B 2018 11 900 130
If want dynamic dictionary by minumal and maximal datetimes use:
s = pd.date_range(df['sale_date'].min(), df['sale_date'].max(), freq='MS')
d = s.year.value_counts().to_dict()
print (d)
{2017: 12, 2018: 11, 2016: 1}
df1 = (df.groupby(['product',df['sale_date'].dt.year], sort=False).sum().add_prefix('total_')
.reset_index())
df1.insert(2,'number_of_months', df1['sale_date'].map(d))
print (df1)
product sale_date number_of_months total_price total_discount
0 A 2016 1 50 5
1 A 2017 12 250 25
2 B 2016 1 200 10
3 B 2017 12 1000 110
4 A 2018 11 100 18
5 B 2018 11 900 130
For ploting is used DataFrame.set_index with DataFrame.unstack:
df2 = (df1.set_index(['sale_date','product'])[['total_price','total_discount']]
.unstack(fill_value=0))
df2.columns = df2.columns.map('_'.join)
print (df2)
total_price_A total_price_B total_discount_A total_discount_B
sale_date
2016 50 200 5 10
2017 250 1000 25 110
2018 100 900 18 130
df2.plot()
EDIT:
df1 = (df.groupby(['product',df['sale_date'].dt.year], sort=False)
.agg( total_price=('price','sum'),
total_discount=('discount','sum'),
number_of_sales=('discount','size'))
.reset_index())
df1.insert(2,'number_of_months', df1['sale_date'].map({2016:1, 2017:12, 2018:11}))
print (df1)
product sale_date number_of_months total_price total_discount \
0 A 2016 NaN 50 5
1 A 2017 NaN 250 25
2 B 2016 NaN 200 10
3 B 2017 NaN 1000 110
4 A 2018 NaN 100 18
5 B 2018 NaN 900 130
number_of_sales
0 1
1 5
2 1
3 5
4 2
5 3