您可以执行以下操作：

import pandas as pd

df1 = pd.DataFrame({'index': [1, 2], 'col_1': ['a', 'b']})
df2 = pd.DataFrame({'index_2': ['A', 'B'], 'col_1': [['a', 'c'], ['a', 'd', 'e']]})

# check for matches
def any_match(list1, list2):
    if list1 is None or list2 is None:
        return False
    return any(x in list2 for x in list1)

# join the dataframes based on matching values
result = pd.merge(df1, df2, how='cross')
result = result[result.apply(lambda x: any_match(x['col_1_x'], x['col_1_y']), axis=1)]

print(result[['index', 'col_1_x', 'index_2', 'col_1_y']])

该函数返回：

index col_1_x index_2    col_1_y
0      1       a       A     [a, c]
1      1       a       B  [a, d, e]

您可以使用explode，然后使用merge，如下所示：

import pandas as pd

# Create the input dataframes
df1 = pd.DataFrame({'index': [1, 2], 'col_1': ['a', 'b']})
df2 = pd.DataFrame({'index_2': ['A', 'B'], 'col_1': [['a', 'c'], ['a', 'd', 'e']]})

# Explode the list column in df2 to multiple rows
df2_exploded = df2.explode('col_1')

# Perform a regular join on the common column
result = df1.merge(df2_exploded, left_on='col_1', right_on='col_1', how='left')

# Get the "col_1" from un-exploded data
result = result.merge(df2, on='index_2', how='left').dropna()

df_exploded看起来像这样：

index_2 col_1
0       A     a
0       A     c
1       B     a
1       B     d
1       B     e

最终的result如下所示：

index col_1_x index_2    col_1_y
0      1       a       A     [a, c]
1      1       a       B  [a, d, e]