將值從一個 dataframe 列復制到另一列

Question

我有兩個數據框 SF 和 OF。

順豐：

   PartNumber  ParentPartNumber   Webname       Brand        Value_Size     Full Description               ImagePath                      Short Description     Weight       RetailPriceEUR           
     2.5            2                Sidi         Si              S     Honeycomb elastic          https://link1,https://link2           Honey               2.3             331           
     2.6            2                Sidi         Si              M     Honeycomb elastic          https://link1,https://link2            Honey              2.3             331           
     2.7            2                Sidi         Si              L     Honeycomb elastic          https://link1,https://link2           Honey               2.3             331          
     3.2            3                Shoei        Sho            S      E.Q.R.S.                   https://link3                          ERQS               1.5             331           
     3.3            3                Shoei        Sho            M      E.Q.R.S.                   https://link3                          ERQS               1.5             331           
     2.9            2                Sidi         Si              XL   Honeycomb elastic           https://link1,https://link2            Honey              2.3             331           

的：

   Type       SKU      Published         Name     Parent        Size     Full Full Description           Image                             ShortDescription       Weight (kg)           Regular Price            Isfeatured           height
 simple        4          1              Bec
  simple       8          1              Lin

我想要做的是在 SF 和 append 中存在的每個重復行之前添加一個額外的行到 OF 數據幀。 例如，如果父項中有重復項，例如 2,2,3,3，則需要復制 2 的第一行和 2 的第二行，並且除了所有行之外，還必須在之前添加一個額外的行他們與描述中的信息。 所以最終結果應該看起來像

結果（附加在 OF 中的 SF 行）：

  Type         SKU      Published         Name      Parent           Size             Full Description           ImagePath                          ShortDescription       Weight           Regular Price            Isfeatured           height
       simple     4          1              Bec
       simple     8          1              Lin
     variable     2          1              Sidi                        S,M,L,XL          Honeycomb elastic         https://link1,https://link2                                                                            yes
    variation     2.5        0              Honey        2               S                                                                                 Honey                2.3              331                        yes
    variation     2.6        0              Honey        2               M                                                                                 Honey                2.3              331                        yes
    variation     2.7        0              Honey        2               L                                                                                 Honey                2.3              331                        yes
    variation     2.9        0              Honey        2               XL                                                                                Honey                2.3              331                        yes
    variable      3 (extra)  1              Sho                         E41,E42                E.Q.R.S.                 https://link3                                                                                       yes
    variation      3          0              ERQS         3               E41                                                                              EQRS                 1.5               33                        yes
    variation      3          0              ERQS         3               E42                                                                              ERQS                 1.5               33                        yes
                 

基本上這就是我想要做的

  if SF ParentPartNumber has duplicates (more than one) AND SF VisibleToCustomer == Y then
    
    Create new row in OF with values as follows  (PARENT PART)
    OF Type = variable
    OF SKU = SF ParentPartNumber
    OF Name = SF WebName
   OF Published = 0
    OF Is featured = yes
    OF Description = SF FullDescription

    OF Images = SF ImagePath (when there is more than 1 link in SF replace | with , (comma))
    OF Attribute 1 value(s) = all values from SF Size that belong to the same ParentPartNumber separated with comma
  
    then below that row copy all rows that belong to this ParentPartNumber as follows   ( all the part numbers under parent part number)
    
    OF Type = variation 
    OF SKU = SF PartNumber
    OF Published = 0
    OF Name = SF ShortDescription 
    OF Weight (kg) = SF Weight
    OF Regular price = SF RetailPriceEUR
    OF Parent = SF ParentPartNumber
     OF Is featured = yes

這就是我嘗試更改代碼的方式

SFs = SF[SF.VisibleToConsumer == 'Y'] 
SFs = SFs[SFs['ParentPartNumber'].duplicated(keep = False)]

def get_group_by_data(df1, name, parent_cols, child_cols, final_col_names):
    df_dict = {col_name: [] for col_name in final_col_names}  # for the final dataframe
    col_names_map = {
        'Type' : 'Type','SKU': 'SKU','WebName': 'Name','Published': 'Published', 'Isfeatured': 'yes', 'ShortDescription': 'Name','FullDescription' :'Description', 
         'Weight': 'Weight (kg)', 'height' : 'height'
          'RetailPriceEUR': 'Regular price', 
          'ImagePath': 'Images','ParentPartNumber': 'Parent',
         'Size': 'Attribute 1 value(s)',
   
    }  # for mapping the output column names to input col names


    # extra row
    parent_comm_cols_n_elems = dict()
    df_dict['Type'].append('variable')
    df_dict['SKU'].append(str(name))
    df_dict['Published'].append(1)
    df_dict['Is featured?'].append('yes')
     df_dict['Parent'].append("")
    for col in parent_cols:
        parent_col_vals = list(dict.fromkeys(list(df1[col])).keys())  # using dictionary for ignoring the duplicate values and still retaining the order
        parent_comm_cols_n_elems[col] = len(parent_col_vals)
        df_dict[col_names_map[col]].append(",".join(val for val in parent_col_vals if val == val))  # val == val for ignoring nan values
    for col in child_cols:
        df_dict[col_names_map[col]].append("")                      
    
    # for adding all the part numbers under parent part number 
    for idx, row in df1.iterrows():
        df_dict['Type'].append('variation')
        df_dict['SKU'].append(row['PartNumber'])
       
        df_dict['Published'].append(1)
        df_dict['Is featured?'].append(0)
       
        df_dict['Parent'].append(str(name))
       
        for col in parent_cols:
            # in case of S,M,L,XL chile rows would have size populated,
            # but in case of 1 elem, like Honeycomb elastic, size not populated in child rows
            if parent_comm_cols_n_elems[col] > 1:
                df_dict[col_names_map[col]].append(row[col])
            else:
                df_dict[col_names_map[col]].append("")
        for col in  child_cols:
            df_dict[col_names_map[col]].append(row[col])
    return pd.DataFrame.from_dict(df_dict)

parent_cols = ['Size', 'FullDescription', 'ImagePath']
#common_cols = ['WebName']
child_cols = ['ShortDescription', 'Weight', 'RetailPriceEUR']

df_append_cols = ['Type', 'SKU', 'Name',  'Published', 'Is featured?',
       'Short description', 'Full Description',
       'Weight (kg)',
        'Height ',', 'Regular price', 'Images',
       'Parent', 'Attribute 1 value(s)']


df_append = pd.DataFrame(SF.groupby('ParentPartNumber')[['PartNumber'] + parent_cols  + child_cols]
                         .apply(lambda x: get_group_by_data(x, x.name, parent_cols,
                                                            child_cols, df_append_cols)).values,  # x.name is 'ParentPartNumber'
                         columns=df_append_cols)
df_append = df_append.fillna('')

Answer 1

def get_group_by_data(df1, name, parent_cols, child_cols, final_col_names):
    df_dict = {col_name: [] for col_name in final_col_names}  # for the final dataframe
    col_names_map = {
        'Type' : 'Type','SKU': 'SKU','WebName': 'Name','Published': 'Published',
        'Isfeatured': 'yes', 'Short Description': 'Name','Full Description' :'Full Description', 
        'Weight': 'Weight (kg)', 'height' : 'height',
        'RetailPriceEUR': 'Regular price', 
        'ImagePath': 'Images','ParentPartNumber': 'Parent',
        'Value_Size': 'Attribute 1 value(s)',
    }  # for mapping the output column names to input col names

    # extra row
#     print(df_dict)
    parent_comm_cols_n_elems = dict()
    df_dict['Type'].append('variable')
    df_dict['SKU'].append(str(name))
    df_dict['Published'].append(1)
    df_dict['Is featured?'].append('yes')
    df_dict['Parent'].append("")
    df_dict['Height'].append("") # added this 
    df_dict['Short Description'].append("") # added this
#     print(f"Parent cols: {parent_cols}")
    for col in parent_cols:
        parent_col_vals = list(dict.fromkeys(list(df1[col])).keys())  # using dictionary for ignoring the duplicate values and still retaining the order
        parent_comm_cols_n_elems[col] = len(parent_col_vals)
#         print(f"parent_cols: {parent_col_vals}")
        df_dict[col_names_map[col]].append(",".join(val for val in parent_col_vals if val == val))  # val == val for ignoring nan values
    for col in child_cols:
        df_dict[col_names_map[col]].append("")                      
    
    # for adding all the part numbers under parent part number 
    for idx, row in df1.iterrows():
        df_dict['Type'].append('variation')
        df_dict['SKU'].append(row['PartNumber'])
        df_dict['Short Description'].append(row['Short Description']) # added this
        df_dict['Published'].append(1)
        df_dict['Is featured?'].append(0)
        df_dict['Height'].append("") # added this
        df_dict['Parent'].append(str(name))
       
        for col in parent_cols:
            # in case of S,M,L,XL chile rows would have size populated,
            # but in case of 1 elem, like Honeycomb elastic, size not populated in child rows
            if parent_comm_cols_n_elems[col] > 1:
                df_dict[col_names_map[col]].append(row[col])
            else:
                df_dict[col_names_map[col]].append("")
        for col in  child_cols:
            df_dict[col_names_map[col]].append(row[col])
#     print(df_dict)
    return pd.DataFrame.from_dict(df_dict)

按“ParentPartNumber”分組並對下面各種列表中的所有列應用分組操作。 基本思想是，當您應用分組依據時，屬於該組的所有行都將作為數據框傳遞（只有您正在應用操作的列）。

SF = SF[SF['ParentPartNumber'].duplicated(keep = False)]

parent_cols = ['Value_Size', 'Full Description', 'ImagePath']
#common_cols = ['WebName']
child_cols = ['Short Description', 'Weight', 'RetailPriceEUR']

df_append_cols = ['Type', 'SKU', 'Name',  'Published', 'Is featured?',
       'Short Description', 'Full Description', 'Weight (kg)',
       'Height', 'Regular price', 'Images','Parent', 'Attribute 1 value(s)']
df_append = pd.DataFrame(SF.groupby('ParentPartNumber')[['PartNumber'] + parent_cols  + child_cols]
                         .apply(lambda x: get_group_by_data(x, x.name, parent_cols,
                                                            child_cols, df_append_cols)).values,  # x.name is 'ParentPartNumber'
                         columns=df_append_cols)
df_append = df_append.fillna('')
df_append