为了提高效率，我建议merging水平的两个嵌套框来处理一个嵌套框。
此外，由于您只希望在所有条件都满足的情况下检索MAKE值，如果其中一个条件不满足，那么比较观察的其他项就没有意义了。这可以保存你很多时间。因此，我引入了一个list_indices变量，它将比较限制为仅对这些值进行比较。
下面是完整的代码：

# imports
import pandas as pd

# Create two DataFrames, df1 and df2, with sample data
df1 = pd.DataFrame({
    'brand': ["winter", "stone", "han", "winter", "stone"],
    'product_line': ["hdw2", "r294", "th22", "trjj", "ael2"],
    'size': ["21.r", "31x5", "3t", "5t", "2d"],
    'profile': ["LINEN", "WAXY", "NaN", "LINEN", "LiNEN"]
})

df2 = pd.DataFrame({
    'NAME': ["Winter", "stoneas", "han", "winter", "stone"],
    'PRODUCT_LINE': ["hdw2", "r294", "th22", "trjj", "ael2"],
    'SIZE': ["21.r", "31x5", "3t", "5t", "2d"],
    'MAKE': [0.2, 0.5, 3, 1, 34]
})

def clean_columns(df, columns):
    df[columns] = df[columns].applymap(lambda x: str(x).lower().replace(" ", ""))

# List of columns to be cleaned
columns_to_clean_df1 = ['brand', 'product_line', 'size']
columns_to_clean_df2 = ['NAME', 'PRODUCT_LINE', 'SIZE']

# lowercase characters and remove spaces
clean_columns(df1, columns_to_clean_df1)
clean_columns(df2, columns_to_clean_df2)

# Merge the two DataFrames horizontally
df_merged = pd.concat([df1, df2], axis=1)

# Define a list of conditions as pairs of columns to compare
compare_cols = [('brand', 'NAME'), ('product_line', 'PRODUCT_LINE'), ('size', 'SIZE')]

# Initialize a list of row indices where 'profile' is not "NaN"
list_indices = df_merged[df_merged['profile'] != "NaN"].index.tolist()

# Create a new column 'conditions' and set it to False
df_merged['conditions'] = False

# Iterate through each pair of columns in 'conditions'
for col1, col2 in compare_cols:
    # Check if the values in the first column of the pair are present in the second column
    df_merged.loc[list_indices, 'conditions'] = df_merged.iloc[list_indices].apply(lambda row: row[col1] in row[col2], axis=1)

    # Update the list of indices to include only the rows where the condition is satisfied
    # as we only want to retrieve the MAKE column if all conditions have been met, 
    # if one of them hasn't, we stop comparing for this row
    list_indices = df_merged[df_merged['conditions']].index.tolist()
    
# Set the 'MAKE' column to None for rows where conditions are not satisfied
df_merged.loc[~df_merged.index.isin(list_indices), "MAKE"] = None

# Create a new DataFrame 'df_result' with selected columns from df1 and 'MAKE' from df2
df_result = df_merged[df1.columns.tolist() + ["MAKE"]]

结果如下：
| | 产品线|大小|轮廓|使| MAKE |
| --|--|--|--|--|--|
| 0 |冬季|HDW2| 21.r|亚麻|0.2|
| 1 |石|r294| 31x5|蜡质|0.5|
| 2 |韩|th22| 3t|楠|楠|
| 3 |冬季|特里季|5t|亚麻| 1 |
| 4 |石|AEL2| 2d|亚麻| 34 |
让我知道这是否符合你的要求！