如何将每行列值与后续行列值相加并创建具有新旧行的新数据框

Question

我有不同产品 id、对应 prod_descriptions 和数量的大数据框。有些产品 id 具有子产品（prod_desc2、prod_desc3...等）要么没有产品 id 要么没有映射到主产品 id（1111,333）并且其值在 prod_id 列中为空，如示例 DF 所示。

Sample DF
prod_id prod_description    col1    col2    col3    col4    col5
1111    prod_desc1          10      20      30      45      25  
        prod_desc2          15      17      16      28      nan
        prod_desc3          15      17      5       nan     nan
2222    prod_desc1          5       10      15      7       10
2223    prod_desc1          15      10      25      10      10
333     prod_desc1          10      15      20      23      25  
        prod_desc2          25      5       25      10      nan

我想将 prod_desc2 和 prod_desc3 的数量汇总到 prod_desc1 级别，并创建一个新的 DF 以及在所需输出中显示的其他 prod _id(2222,2223)。这样每个产品 ID 将有其子产品的一行累积总和.

Desired Output 
prod_id prod_description    col1    col2    col3    col4    col5
1111    prod_desc1          40      54      51      73      25  
2222    prod_desc1          5       10      15      7       10
2223    prod_desc1          15      10      25      10      10
333     prod_desc1          35      20      45      33      25  

下面是我尝试过的“部分”代码，但在对 prod id 行和 no_prod_id 列的列值求和并将它们与其他 prod_id 一起保存在新数据框中时遇到了麻烦。请执行需要。

Empty rows were filled with no_prod_id 

prod_id     prod_description    col1    col2    col3    col4    col5
1111        prod_desc1          10      20      30      45      25  
no_prod_id  prod_desc2          15      17      16      28      nan
no_prod_id  prod_desc3          15      17      5       nan     nan
2222        prod_desc1          5       10      15      7       10
2223        prod_desc1          15      10      25      10      10
333         prod_desc1          10      15      20      23      25  
no_prod_id  prod_desc2          25      5       25      10      nan

null_value_count=[]
rolled_up_values=[]
for i in df.index:
    if df.iloc[i,0]=="no_prod_id": #pick no_prod_id row
        x=df.iloc[i,:]  #save null value row
        if x.isnull().sum().sum()==df.shape[1]: # check if no_prod_id is having all nulls 
            null_value_cunt.append(i)         #save index for later deleting it from DF
        else:
            if df.iloc[i-1,0]!= "no_prod_id": #check previus row has main prod id 
                y=df.iloc[i-1,:] # save main prod id row
                for val in  range(1,len(y)):    #get each value of main prod id 
                    rolled_up_values.append(x[val]+y[val]) #sum with no_prod_id value save the out in 
                                                           #list for updating in a new DF

Answer 1

第一次ffill

df['prod_id'] = df['prod_id'].ffill()
print(df)
    prod_id prod_description  col1  col2  col3  col4  col5
0   1111.0       prod_desc1    10    20    30  45.0  25.0
1   1111.0       prod_desc2    15    17    16  28.0   NaN
2   1111.0       prod_desc3    15    17     5   NaN   NaN
3   2222.0       prod_desc1     5    10    15   7.0  10.0
4   2223.0       prod_desc1    15    10    25  10.0  10.0
5    333.0       prod_desc1    10    15    20  23.0  25.0
6    333.0       prod_desc2    25     5    25  10.0   NaN

---

然后我们删除您的 prod_description 和 groupby 剩余的列，

df_new = df.drop('prod_description',axis=1).groupby('prod_id').sum().reset_index()

df_new.insert(1,'prod_description','prod_desc1') # reinsert columns. 

---

结果，请注意我刚刚添加了自定义排序以匹配您的输出。

idx = df_new['prod_id'].astype(str).str[1].astype(int).sort_values().index
print(df_new.loc[idx])
   prod_id prod_description  col1  col2  col3  col4  col5
1   1111.0       prod_desc1    40    54    51  73.0  25.0
2   2222.0       prod_desc1     5    10    15   7.0  10.0
3   2223.0       prod_desc1    15    10    25  10.0  10.0
0    333.0       prod_desc1    35    20    45  33.0  25.0

或者如 anky_91 善意指出的那样，我们可以通过使用.assign和sort=False将代码行减少到简单的两行

df['prod_id'] = df['prod_id'].ffill()
df.groupby("prod_id", sort=False, as_index=False).sum().assign(
prod_description="prod_desc1"
).reindex(df.columns, axis=1)

---

结果

   prod_id prod_description  col1  col2  col3  col4  col5
0   1111.0       prod_desc1    40    54    51  73.0  25.0
1   2222.0       prod_desc1     5    10    15   7.0  10.0
2   2223.0       prod_desc1    15    10    25  10.0  10.0
3    333.0       prod_desc1    35    20    45  33.0  25.0