Overall sum by groupby pandas - pandas

Question

I have a dataframe as shown below, which is area usage of whole city say Bangalore.
Sector Plot Usage Status Area
A 1 Villa Constructed 40
A 2 Residential Constructed 50
A 3 Substation Not_Constructed 120
A 4 Villa Not_Constructed 60
A 5 Residential Not_Constructed 30
A 6 Substation Constructed 100
B 1 Villa Constructed 80
B 2 Residential Constructed 60
B 3 Substation Not_Constructed 40
B 4 Villa Not_Constructed 80
B 5 Residential Not_Constructed 100
B 6 Substation Constructed 40
Bangalore consist of two sectors A and B.
From the above I would like to calculate total area of Bangalore and its distribution of usage.
Expected Output:
City Total_Area %_Villa %_Resid %_Substation %_Constructed %_Not_Constructed
Bangalore(A+B) 800 32.5 30 37.5 46.25 53.75

Answer 1

I think you need set scalar value to column city before apply solution (if there are only sectors A and B):
df['Sector'] = 'Bangalore(A+B)'
#aggregate sum per 2 columns Sector and Usage
df1 = df.groupby(['Sector', 'Usage'])['Area'].sum()
#percentage by division of total per Sector
df1 = df1.div(df1.sum(level=0), level=0).unstack(fill_value=0).mul(100).add_prefix('%_')
#aggregate sum per 2 columns Sector and Status
df2 = df.groupby(['Sector', 'Status'])['Area'].sum()
df2 = df2.div(df2.sum(level=0), level=0).unstack(fill_value=0).mul(100).add_prefix('%_')
#total Area per Sector
s = df.groupby('Sector')['Area'].sum().rename('Total_area')
#join all together
dfA = pd.concat([s, df1, df2], axis=1).reset_index()
print (dfA)
Sector Total_area %_Residential %_Substation %_Villa \
0 Bangalore(A+B) 800 30.0 37.5 32.5
%_Constructed %_Not_Constructed
0 46.25 53.75

Answer 2

Simple Pivot Table can help!
1. One Line Pandas Solution: 80% work done
pv =
df.pivot_table(values='Area',aggfunc=np.sum,index=['Status'],columns=['Usage'],margins=True,margins_name='Total',fill_value=0).unstack()
2. Now formatting for %: 90% work done
ans =
pd.DataFrame([[pv['Villa']['Total']/pv['Total']['Total'].astype('float'),pv['Resid']['Total']/pv['Total']['Total'].astype('float'),pv['Substation']['Total']/pv['Total']['Total'].astype('float'),pv['Total']['Constructed']/pv['Total']['Total'].astype('float'),pv['Total']['Not_Constructed']/pv['Total']['Total'].astype('float')]]).round(2)*100
3. Adding the Total Column: 99% work done
ans['Total'] = pv['Total']['Total']
4. Renaming Columns and Arranging in your expected order: and done!
ans.columns=['%_Villa','%_Resid','%_Substation','%_Constructed','%_Not_Constructed','Total']
ans = ans[['Total',''%_Villa','%_Resid','%_Substation','%_Constructed','%_Not_Constructed']]