Python 作用于数据透视框的数据透视表

我有一个数据帧，它已经被旋转，看起来像下面这样

             Cost      Transport     Currency        
Manufacturer  ABC  XYZ       ABC XYZ      ABC     XYZ
Date                                                 
2017-07-01    312  323        31  41   Pounds  Pounds
2017-07-02    423  335        21  32  Dollars  Pounds
2017-07-03    421  304        21  21  Dollars  Pounds

上面显示了与从制造商处购买物品相关的成本和运输费用，以及成本和费用的计价货币

我想做的是把这些数字加起来，放在货币下面。所需的输出是（我未对加法进行评估，以便清楚它来自何处）

我试过了

df.pivot_table(index='Date', columns='Currency', aggfunc=np.sum)

熊猫根本不喜欢给出一个键错误

下面是获取起始数据帧df的代码。在实际用例中，数据绝对需要首先进行数据透视以进行分析和聚合，因此请不要建议在my_列表或df_raw上应用数据透视表

my_list = ["2017-07-01", "ABC",312, 31, "Pounds",  "2017-07-01", "XYZ" ,323, 41, "Pounds",
           "2017-07-02", "ABC", 423, 21, "Dollars", "2017-07-02", "XYZ" ,335, 32, "Pounds",
           "2017-07-03", "ABC", 421, 21, "Dollars", "2017-07-03", "XYZ", 304, 21, "Pounds" ]
df_raw = pd.DataFrame(np.array(my_list).reshape(6,5),
                   columns = ["Date", "Manufacturer", "Cost", "Transport", "Currency"])
df = df_raw.pivot(index='Date', columns='Manufacturer')

编辑2：修订

df2 = df.stack()
df2['total'] = df2['Cost'] + df2['Transport'] 
df2.reset_index(inplace = True)
df2.pivot_table(index = 'Date', columns = 'Currency', values = 'total', aggfunc = np.sum, fill_value = 0)

编辑：下面的答案对于你所看到的事实上是不可接受的。将尝试修改

单程

df_raw['total_cost'] = df_raw['Cost'] + df_raw['Transport']
df_raw.pivot_table(index = 'Date', columns = 'Currency', values = ['total_cost'], aggfunc = 'sum', fill_value = 0)

---

使用

stack

，

groupby

，

sum

，

unstack

：

使用您的设置和输入数据框：

my_list = ["2017-07-01", "ABC",312, 31, "Pounds",  "2017-07-01", "XYZ" ,323, 41, "Pounds",
           "2017-07-02", "ABC", 423, 21, "Dollars", "2017-07-02", "XYZ" ,335, 32, "Pounds",
           "2017-07-03", "ABC", 421, 21, "Dollars", "2017-07-03", "XYZ", 304, 21, "Pounds" ]
df_raw = pd.DataFrame(np.array(my_list).reshape(6,5),
                   columns = ["Date", "Manufacturer", "Cost", "Transport", "Currency"])
df = df_raw.pivot(index='Date', columns='Manufacturer')

df = df.apply(pd.to_numeric,errors='ignore')

重塑数据帧并计算：

df.stack().groupby(['Date','Currency']).sum().sum(1).unstack(fill_value=0)

输出：

Currency    Dollars  Pounds
Date                       
2017-07-01        0     707
2017-07-02      444     367
2017-07-03      442     325

---

谢谢stack、groupby、sum和unstack的使用令人印象深刻。：-）

Currency    Dollars  Pounds
Date                       
2017-07-01        0     707
2017-07-02      444     367
2017-07-03      442     325