我有一个数据集，其中包含来自不同供应商，地点，日期和产品的多个销售注册。数据集如下：
local categoria fabricante tipo consistencia peso pacote ordem vendas_kg
AREA I SABAO ASATP DILUIDO LIQUIDO 1501 A 2000g PLASTICO 1 10
AREA I SABAO TEPOS DILUIDO LIQUIDO 1501 A 2000g PLASTICO 1 20
AREA I SABAO ASATP CAPSULA LIQUIDO 1501 A 2000g PLASTICO 1 20
AREA I SABAO TEPOS CAPSULA LIQUIDO 1501 A 2000g PLASTICO 1 30
AREA I SABAO ASATP DILUIDO LIQUIDO 1501 A 2000g PLASTICO 2 20
AREA I SABAO TEPOS DILUIDO LIQUIDO 1501 A 2000g PLASTICO 2 30
AREA I SABAO ASATP CAPSULA LIQUIDO 1501 A 2000g PLASTICO 2 20
AREA I SABAO TEPOS CAPSULA LIQUIDO 1501 A 2000g PLASTICO 2 30
AREA II SABAO ASATP DILUIDO LIQUIDO 1501 A 2000g PLASTICO 1 10
AREA II SABAO TEPOS DILUIDO LIQUIDO 1501 A 2000g PLASTICO 1 15
AREA II SABAO ASATP CAPSULA LIQUIDO 1501 A 2000g PLASTICO 1 25
AREA II SABAO TEPOS CAPSULA LIQUIDO 1501 A 2000g PLASTICO 1 35
AREA II SABAO ASATP DILUIDO LIQUIDO 1501 A 2000g PLASTICO 2 20
AREA II SABAO TEPOS DILUIDO LIQUIDO 1501 A 2000g PLASTICO 2 25
AREA II SABAO TEPOS CAPSULA LIQUIDO 1501 A 2000g PLASTICO 2 20
AREA II SABAO TEPOS CAPSULA LIQUIDO 1501 A 2000g PLASTICO 2 30
我正在使用以下代码来转动此数据集：
temp_df = pd.pivot_table(df,index=['local','tipo','ordem'], values=['vendas_kg'] , aggfunc=[np.sum], columns=['fabricante'], fill_values=0, margins=True, margins_name= 'Total')
我得到这个输出：
sum sum
vendas_kg vendas_kg
fabricante ASATP TEPOS Total
local tipo ordem
AREA I DILUIDO 1 10 20 30
2 20 30 50
CAPSULA 1 10 20 30
2 20 30 50
AREA II DILUIDO 1 10 15 25
2 20 25 45
CAPSULA 1 25 35 55
2 20 30 50
我想计算每个['ordem']的百分比和每个段的百分比。 ordem，tipo和local，像这样：
sum sum
vendas_kg vendas_kg
fabricante ASATP TEPOS % segment Total
local tipo ordem
AREA I DILUIDO 1 33% 66% 50% 30
2 40% 60% 50% 50
CAPSULA 1 33% 66% 50% 30
2 40% 60% 50% 50
AREA II DILUIDO 1 40% 60% 31.25% 25
2 44.44% 55.56% 47.37% 45
CAPSULA 1 43.64% 57.36% 53.63% 55
2 40% 60% 53.63% 50
因此，AREA I DILUIDO 1的总销售额为30，ASATP销售额占其中的33％，TEPOS占66％，而根据AREA I 1的总销售额，DILUIDO销售额占50％，依此类推。
我还想比较['ordem']之间的销售差异，例如段和['fabricante']的百分比增长并存储在如下的新表中：
% change in % change in
vendas_kg vendas_kg % change in % change in
fabricante ASATP TEPOS % segment Total
AREA I DILUIDO 1 0 0 0 0
2 +7% -6% 0 20
3 0 0 0 0
AREA I CAPSULA 1 0 0 0 0
2 +7% -6% 0 20
3 0 0 0 0
AREA II DILUIDO 1 0 0 0 0
2 +4.44% -4.44% +16.12% 20
3 0 0 0 0
AREA II CAPSULA 1 0 0 0 0
2 -3.64% +3.64% 0 5
3 0 0 0 0
在过去的5天里我一直坚持这一点，我在['fabricante'] ['tipo']和['local']中有更多类别，所以它必须适用于每个类别中的两个以上类别。感谢您的帮助，如有疑问，请随时与我联系。
取百分比：
df_percent = temp_df.iloc[:, [0,1]].apply(lambda x: round(x / x.sum() * 100, 2), axis = 1)
采取变化，使用diff
df_diff_percent = df_percent.groupby(level=[0,1]).diff().fillna(0)
sum
vendas_kg
fabricante ASATP TEPOS
local tipo ordem
AREA I CAPSULA 1 0.00 0.00
2 0.00 0.00
DILUIDO 1 0.00 0.00
2 6.67 -6.67
AREA II CAPSULA 1 0.00 0.00
2 -41.67 41.67