Pandas解决方案
要使用的第一个转换列表 DataFrame.explode :

new_df=df.copy()
new_df['CITY']=new_df['CITY'].str.lstrip('|').str.split('|')

# print(new_df)

# ID                                CITY

# 0   1              [London, Paris, Tokyo]

# 1   2  [Tokyo, Barcelona, Mumbai, London]

# 2   3            [Vienna, Paris, Seattle]

然后我们可以使用：
方法1： DataFrame.pivot_table ```
new_df=( new_df.explode('CITY')
.pivot_table(columns='CITY',index='ID',aggfunc='size',fill_value=0)
[city_of_interest]
.reset_index()
.rename_axis(columns=None)
)
print(new_df)

方法2： `DataFrame.groupby` +  `DataFrame.unstack` ```
new_df=( new_df.explode('CITY')
               .groupby(['ID'])
               .CITY
               .value_counts()
               .unstack('CITY',fill_value=0)[city_of_interest]
               .reset_index()
               .rename_axis(columns=None)

                )
print(new_df)

输出新数据框：

ID  Paris  Seattle  Tokyo
0   1      1        0      1
1   2      0        0      1
2   3      1        1      0

使用自定义项检查“感兴趣的城市”值是否位于分隔列中。

from pyspark.sql.functions import udf

# Input list

city_of_interest=['Paris','Seattle','Tokyo']

# UDF definition

def city_present(city_name,city_list):
    return len(set([city_name]) & set(city_list.split('|')))

city_present_udf = udf(city_present,IntegerType())

# Converting cities list to a column of array type for adding columns to the dataframe

city_array = array(*[lit(city) for city in city_of_interest])
l = len(city_of_interest)
col_names = df.columns + [city for city in city_of_interest]
result = df.select(df.columns + [city_present_udf(city_array[i],df.city) for i in range(l)])
result = result.toDF(*col_names)
result.show()

对于pyspark，使用split+array\u contains：

from pyspark.sql.functions import split, array_contains

df.withColumn('cities', split('CITY', '\|')) \
  .select('ID', *[ array_contains('cities', c).astype('int').alias(c) for c in city_of_interest ]) 
  .show()
+---+-----+-------+-----+
| ID|Paris|Seattle|Tokyo|
+---+-----+-------+-----+
|  1|    1|      0|    1|
|  2|    0|      0|    1|
|  3|    1|      1|    0|
+---+-----+-------+-----+

对于Pandas，请使用series.str.get\u dummies：

df[city_of_interest] = df.CITY.str.get_dummies()[city_of_interest]
df = df.drop('CITY', axis=1)