Question

我有一个如下所示的数据框

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在上面的数据框中，我想在下面的数据框中进行准备。

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Answer 1

在此需要将GroupBy.agg与聚合函数字典一起使用，此处DataFrameGroupBy.nunique和DataFrameGroupBy.size用于计数：

#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+解决方案：

#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

Answer 2

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

分组求和，根据条件计数并以大熊猫串联

2 个答案: