Pandas 按熊猫分组的总金额

我有一个数据框，如下所示，这是整个城市的区域使用情况，比如班加罗尔

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

班加罗尔由A区和B区组成

根据以上内容，我想计算班加罗尔的总面积及其使用分布

预期产出：

City           Total_Area  %_Villa  %_Resid  %_Substation  %_Constructed  %_Not_Constructed
Bangalore(A+B) 800         32.5     30       37.5          46.25          53.75

---

我认为您需要在应用解决方案之前将标量值设置为列

city

（如果只有扇区

A

和

B

）：

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简单数据透视表可以帮助您

1。一线解决方案：完成80%的工作

pv=
df.pivot_表（values='Area'，aggfunc=np.sum，index=['Status']，columns=['Usage']，margins=True，margins_name='Total'，fill_value=0）。取消堆栈（）

2。现在为%设置格式：完成90%的工作

ans=
pd.数据框（[[pv['Villa']['Total']/pv['Total']['Total'].aType（'float'）、pv['Resid']['Total']/pv['Total'].aType（'float'）、pv['Substation']['Total']['Total'].aType（'float'）、pv['Total']['Constructed']/pv['Total'.['Total'].aType（'float'）、pv['Total'.['Total']*100

3。添加总列：99%的工作量已完成

ans['Total']=pv['Total']['Total']

4。重命名列并按预期顺序排列：完成

ans.columns=['%\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\

ans=ans[[总计，'%'别墅'，'%'住宅'，'%'变电站'，'%'已施工'，'%'未施工']

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