Question

我有一个具有不同产品ID，对应prod_descriptions和数量的大数据框。有些产品ID包含子产品（prod_desc2，prod_desc3 ...等）没有产品ID或未映射到主产品ID（1111），333），其值在prod_id列中为空，如示例DF中所示。

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，并在所需的输出中显示一个新的DF以及其他prod _id（2222,2223），以便每个产品ID都有一行的累加和其子产品。

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，在累加prod id row和no_prod_id列的列值并将它们与其他prod_ids一起保存到新的数据框中时遇到了麻烦。

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

第一个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并按剩余的列进行分组

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

或正如anky_91所指出的那样，我们可以使用.assign和sort=False

将代码行简化为简单的两行代码

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

如何将每个行列的值求和后续的行列的值，以及如何使用新行和旧行创建新的数据框

1 个答案: