Python function to perform calculation among each group of data frame - python-3.x

Question

I need to have a function which performs below mentioned action ;
The dataset is :
and output expected is value in 'Difference' column , where remaining are input column.
Please note that within each group we first need to identify the maximum 'Closing_time' and the corrosponding amount will be the maximum value for that period , and then each row value will be subtracted from maximum detected value of previous period and result would be difference for that cell.
Also in case if the record do not have previous period then max value will be NA and difference caculation would be NA for all record for that period,
Adding points - within in each group (Cost_centre, Account, Year, Month) - Closing_time values are like ( D-0 00 CST is min and D-0 18 CST is maximim , similary within D-0,D+1, D+3 etc - D+3 will be maximum)
I tried to find first if previous value exist for each of the group or not and then find maximum time within each period and then crrosponding amount value to it.
Further using the maximum value , tried to subtract record Amount from Maximum value ,
but not getting how to implement , kindly help.

Answer 1

post sharing the above question i came up for this solution.
I splitted this in 3 part -
a) First find previous year and month for each of cost_center and account
b) Find maximum Closing_time within each group of cost_cente,account, year and month. Then pick corrosponding Amount value as amount .
c) using amount coming from b , subtract current amount with b to get diffrence.
def prevPeriod(df):
period =[]
for i in range(df.shape[0]):
if df['Month'][i]==1:
val_year = df['Year'][i]-1
val_month = 12
new_val =(val_year,val_month)
period.append(new_val)
else:
val_year = df['Year'][i]
val_month = df['Month'][i]-1
new_val =(val_year,val_month)
period.append(new_val)
print(period)
df['Previous_period'] = period
return df
def max_closing_time(group_list):
group_list = [item.replace('CST','') for item in group_list]
group_list = [item.replace('D','') for item in group_list]
group_list = [item.split()[:len(item)] for item in group_list]
l3 =[]
l4 =[]
for item in group_list:
l3.append(item[0])
l4.append(item[1])
l3 =[int(item) for item in l3]
l4 = [int(item) for item in l4]
max_datevalue = max(l3)
max_datevalue_index = l3.index(max(l3))
max_time_value = max(l4[max_datevalue_index:])
maximum_period = 'D+'+str(max_datevalue)+' '+str(max_time_value)+' '+'CST'
return maximum_period
def calculate_difference(df):
diff =[]
for i in range(df.shape[0]):
prev_year =df['Previous_period'][i][0]
print('prev_year is',prev_year)
prev_month = df['Previous_period'][i][1]
print('prev_month is', prev_month)
max_closing_time = df[(df['Year']==prev_year)& (df['Month']==prev_month)]['Max_Closing_time']
print('max_closing_time is', max_closing_time)
#max_amount_consider = df[(df['Year']==prev_year)& (df['Month']==prev_month) &(df['Max_Closing_time']==max_closing_time)]['Amount']
if bool(max_closing_time.empty):
found_diff = np.nan
diff.append(found_diff)
else:
max_closing_time_value = list(df[(df['Year']==prev_year)& (df['Month']==prev_month)]['Max_Closing_time'])[0]
max_amount_consider = df[(df['Cost_centre']==df['Cost_centre'][i])&(df['Account']==df['Account'][i])&(df['Year']==prev_year) & (df['Month']==prev_month) &(df['Closing_time']==str(max_closing_time_value))]['Amount']
print('max_amount_consider is',max_amount_consider)
found_diff = int(max_amount_consider) - df['Amount'][i]
diff.append(found_diff)
df['Variance'] = diff
return df
def calculate_variance(df):
'''
Input data frame is coming as query used above to fetch data
'''
try:
df = prevPeriod(df)
except:
print('Error occured in prevPeriod function')
# prerequisite for max_time_period
df2 = pd.DataFrame(df.groupby(['Cost_centre','Account','Year','Month'])['Closing_time'].apply(max_closing_time).reset_index())
df = pd.merge(df,df2, on =['Cost_centre','Account','Year','Month'])
# final calculation
try:
final_result = calculate_difference(df)
except:
print('Error in calculate_difference')
return final_result