Pandas Groupby sum，根据条件计算数字，并按顺序连接

我有一个如下所示的数据帧

Sector Property_ID  Unit_ID  Unit_usage   Property_Usage  Rent_Unit_Status  Unit_Area
SE1    1            1        Shop         Commercial      Rented            200
SE1    1            2        Resid        Commercial      Rented            200
SE1    1            3        Shop         Commercial      Vacant            100
SE1    2            1        Shop         Residential     Vacant            200
SE1    2            2        Apartment    Residential     Rented            100
SE2    1            1        Resid        Commercial      Rented            400
SE2    1            2        Shop         Commercial      Vacant            100
SE2    2            1        Apartment    Residential     Vacant            500

从上面的数据框中，我想准备下面的数据框

Sector  No_of_Properties  No_of_Units  Total_area  %_Vacant   %_Rented  %_Shop  %_Apartment
SE1     2                 5            800         37.5       62.5      62.5    12.5
SE2     2                 3            1000        60         40        10      50

这里是聚合函数字典的必要用法，这里和计数：

#aggregate sum per 2 columns Sector and Usage
df1 = df.groupby(['Sector', 'Unit_usage'])['Unit_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', 'Rent_Unit_Status'])['Unit_Area'].sum()
df2 = df2.div(df2.sum(level=0), level=0).unstack(fill_value=0).mul(100).add_prefix('%_')
#aggregations
s = df.groupby('Sector').agg({'Property_ID':'nunique','Unit_ID':'size', 'Unit_Area':'sum'})
s = s.rename(columns={'Property_ID':'No_of_Properties','Unit_ID':'No_of_Units',
                      'Unit_Area':'Total_area'})
#join all together
df = pd.concat([s, df1, df2], axis=1).reset_index()
print (df)
  Sector  No_of_Properties  No_of_Units  Total_area  %_Apartment  %_Resid  \
0    SE1                 2            5         800         12.5     25.0   
1    SE2                 2            3        1000         50.0     40.0   

   %_Shop  %_Rented  %_Vacant  
0    62.5      62.5      37.5  
1    10.0      40.0      60.0  

熊猫0.25+溶液：

#aggregate sum per 2 columns Sector and Usage
df1 = df.groupby(['Sector', 'Unit_usage'])['Unit_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', 'Rent_Unit_Status'])['Unit_Area'].sum()
df2 = df2.div(df2.sum(level=0), level=0).unstack(fill_value=0).mul(100).add_prefix('%_')
#aggregations
s = df.groupby('Sector').agg(No_of_Properties=('Property_ID','nunique'),
                             No_of_Units=('Unit_ID','size'),
                             Total_area= ('Unit_Area','sum'))
#join all together
df = pd.concat([s, df1, df2], axis=1).reset_index()
print (df)

  Sector  No_of_Properties  No_of_Units  Total_area  %_Apartment  %_Resid  \
0    SE1                 2            5         800         12.5     25.0   
1    SE2                 2            3        1000         50.0     40.0   

   %_Shop  %_Rented  %_Vacant  
0    62.5      62.5      37.5  
1    10.0      40.0      60.0  

---

更新：现在计算总面积的百分比

您可以为此使用

pd.groupby.apply

def summarise(df):
    output = pd.Series()
    output['No_of_Properties'] = df['Property_ID'].nunique()
    output['No_of_Units'] = df['Unit_ID'].size
    output['Total_area'] = df['Unit_Area'].sum()
    output['%_Rented'] = (df['Unit_Area'].loc[df['Rent_Unit_Status'] == 'Rented'].sum() / output['Total_area']) * 100
    output['%_Shop'] = (df['Unit_Area'].loc[df['Unit_usage'] == 'Shop'].sum() / output['Total_area']) * 100
    output['%_Apartment'] = (df['Unit_Area'].loc[df['Unit_usage'] == 'Apartment'].sum() / output['Total_area']) * 100

    return output

print(df.groupby('Sector').apply(summarise))

输出：

No_of_Properties  No_of_Units  Total_area  %_Rented  %_Shop  \
Sector                                                                
SE1                  2.0          5.0       800.0      62.5    62.5   
SE2                  2.0          3.0      1000.0      40.0    10.0   

        %_Apartment  
Sector               
SE1            12.5  
SE2            50.0  

---

您的pandas版本是什么？为什么第一个扇区租用了

%\u

62.5%？不应该是60%？@jezrael我的熊猫版-0.25。1@mrzo (200+200+100)/800 *100 = 62.5