Calculating based on date pandas - python-3.x

Question

I have this dataframe:
a = [1, 2, 3, 4, 5]
b = ['2019-08-01', '2019-09-01', '2019-10-23', '2019-11-12', '2019-11-30']
c = [12, 0, 0, 0, 0]
d = [0, 23, 0, 0, 0]
e = [12, 24, 35, 0, 0]
f = [0, 0, 44, 56, 82]
g = [21, 22, 17, 75, 63]
df = pd.DataFrame({'ID': a, 'Date': b, 'Unit_sold_8': c,
'Unit_sold_9': d, 'Unit_sold_10': e, 'Unit_sold_11': f,
'Unit_sold_12': g})
df['Date'] = pd.to_datetime(df['Date'])
I want to calculate average sales of each ID which are based on Date. For example, If ID's open date was on Sep, so the average sales of this ID would start on Sep. I tried np.select but I realized that this method would make my code super long.
col = df.columns
mask1 = (df['Date'] >= "08/01/2019") & (df['Date'] < "09/01/2019")
mask2 = (df['Date'] >= "09/01/2019") & (df['Date'] < "10/01/2019")
mask3 = (df['Date'] >= "10/01/2019") & (df['Date'] < "11/01/2019")
mask4 = (df['Date'] >= "11/01/2019") & (df['Date'] < "12/01/2019")
mask5 = (df['Date'] >= "12/01/2019")
condition2 = [mask1, mask2, mask3, mask4, mask5]
result2 = [df[col[2:]].mean(skipna = True, axis = 1),
df[col[3:]].mean(skipna = True, axis = 1),
df[col[4:]].mean(skipna = True, axis = 1),
df[col[5:]].mean(skipna = True, axis = 1),
df[col[6:]].mean(skipna = True, axis = 1)]
df.loc[:, 'Mean'] = np.select(condition2, result2, default = np.nan)
Are there any way faster to solve this problem? Especially when the time range is expanded (12 months, 24 months, .etc)

Answer 1

Does it help you?
from datetime import datetime
import numpy as np
from dateutil import relativedelta
check_date = datetime.today()
df['n_months'] = df['Date'].apply(lambda x: relativedelta.relativedelta( check_date,x).months)
df['total'] = df.iloc[:,range(2,df.shape[1]-1)].sum(axis=1)
df['avg'] = df['total'] / df['n_months']
print(df)
ID Date Unit_sold_8 ... n_months total avg
0 1 2019-08-01 12 ... 5 45 9.00
1 2 2019-09-01 0 ... 4 69 17.25
2 3 2019-10-23 0 ... 3 96 32.00
3 4 2019-11-12 0 ... 2 131 65.50
4 5 2019-11-30 0 ... 2 145 72.50

Answer 2

M= df
#melt data to pull units as variables
.melt(id_vars=['ID','Date'])
#create temp variables to pull out Month from Date and Units
.assign(Mth=lambda x: x['Date'].dt.month,
oda_detail = lambda x: x.variable.str.split('_').str[-1])
.sort_values(['ID','Mth'])
#keep only rows where the Mth is less than or equal to other detail
.loc[lambda x : x['Mth'].astype(int).le(x['oda_detail'].astype(int))]
#groupby and get the mean
.groupby(['ID','Date'])['value'].mean()
.reset_index()
.drop(['ID','Date'],axis=1)
.rename({'value':'Mean'},axis=1)
Join back to original dataframe:
pd.concat([df,M],axis=1)
ID Date Unit_sold_8 Unit_sold_9 Unit_sold_10 Unit_sold_11
Unit_sold_12 Mean
0 1 2019-08-01 12 0 12 0 21 9.00
1 2 2019-09-01 0 23 24 0 22 17.25
2 3 2019-10-23 0 0 35 44 17 32.00
3 4 2019-11-12 0 0 0 56 75 65.50
4 5 2019-11-30 0 0 0 82 63 72.50