带有列的数据透视表pandas中的百分比计算

问题描述 投票:2回答:1

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。

python pandas analytics pandas-groupby
1个回答
1
投票

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
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