我有一个包含以下数据的熊猫系列：
2015-07-24 ['Business', 'Corporate']
2015-07-24 ['Business', 'Corporate']
2015-07-08 ['Commentary', 'World']
2015-07-05 ['Issues', 'JUST BE CAUSE']
2015-06-15 ['Business']
2015-04-11 ['Business', 'Economy']
2015-03-24 ['Food & Drink']
2015-02-24 ['Commentary', 'Japan']
2015-02-19 ['Business', 'Financial Markets']
2015-01-05 ['National', '70 YEARS OF PEACE AND PROSPERITY']
2014-11-06 ['National']
2014-10-31 ['Business', 'Financial Markets']
2014-10-20 ['Business']
2014-09-22 ['Business', 'Economy']
2014-09-02 ['Business', 'Corporate']
2014-08-08 ['Business', 'Financial Markets']
2014-07-18 ['Business', 'Financial Markets', 'TSE DATA & ...
2014-07-15 ['Business', 'Economy']
2014-07-10 ['National']
2013-12-16 ['Business', 'Financial Markets']
2013-10-29 ['National']
2013-10-15 ['National']
2013-10-06 ['Business', 'Financial Markets']
2013-06-25 ['Business', 'Financial Markets']
2013-06-17 ['Editorials']
2013-05-21 ['Voices', 'VIEWS FROM THE STREET']
2013-05-15 ['Business', 'Financial Markets']
2013-05-03 ['National']
2013-03-07 ['Business', 'Financial Markets']
2013-02-10 ['Business', 'Economy']
Name: tags, Length: 216, dtype: object
有没有办法得到一个布尔数组告诉我每个列表是否包含“Business”或“Food＆amp; Drink”？
答案 0 :(得分：2)
如果值为list s，请使用：
print (df)
tags
2015-07-24 ['Business','Corporate']
2015-07-24 ['Business','Food & Drink']
2015-07-08 ['Commentary','World']
2015-07-05 ['Issues','JUST BE CAUSE']
2015-06-15 ['Business']
2015-04-11 ['Business','Food & Drink', 'a']
val = set(['Business','Food & Drink'])
m = df['tags'].apply(lambda x: set(x) >= val)
print (m)
2015-07-24 False
2015-07-24 True
2015-07-08 False
2015-07-05 False
2015-06-15 False
2015-04-11 True
Name: tags, dtype: bool
答案 1 :(得分：1)
使用set
df.tags.apply(set) & set(['Business', 'Food & Drink'])
2015-07-24 True
2015-07-24 True
2015-07-08 False
2015-07-05 False
2015-06-15 True
2015-04-11 True
2015-03-24 True
2015-02-24 False
2015-02-19 True
2015-01-05 False
2014-11-06 False
2014-10-31 True
2014-10-20 True
2014-09-22 True
2014-09-02 True
2014-08-08 True
2014-07-18 True
2014-07-15 True
2014-07-10 False
2013-12-16 True
2013-10-29 False
2013-10-15 False
2013-10-06 True
2013-06-25 True
2013-06-17 False
2013-05-21 False
2013-05-15 True
2013-05-03 False
2013-03-07 True
2013-02-10 True
Name: tags, dtype: bool
并排查看结果
df.assign(i=df.tags.apply(set) & set(['Business', 'Food & Drink']))
tags i
2015-07-24 [Business, Corporate] True
2015-07-24 [Business, Corporate] True
2015-07-08 [Commentary, World] False
2015-07-05 [Issues, JUST BE CAUSE] False
2015-06-15 [Business] True
2015-04-11 [Business, Economy] True
2015-03-24 [Food & Drink] True
2015-02-24 [Commentary, Japan] False
2015-02-19 [Business, Financial Markets] True
2015-01-05 [National, 70 YEARS OF PEACE AND PROSPERITY] False
2014-11-06 [National] False
2014-10-31 [Business, Financial Markets] True
2014-10-20 [Business] True
2014-09-22 [Business, Economy] True
2014-09-02 [Business, Corporate] True
2014-08-08 [Business, Financial Markets] True
2014-07-18 [Business, Financial Markets, TSE DATA &] True
2014-07-15 [Business, Economy] True
2014-07-10 [National] False
2013-12-16 [Business, Financial Markets] True
2013-10-29 [National] False
2013-10-15 [National] False
2013-10-06 [Business, Financial Markets] True
2013-06-25 [Business, Financial Markets] True
2013-06-17 [Editorials] False
2013-05-21 [Voices, VIEWS FROM THE STREET] False
2013-05-15 [Business, Financial Markets] True
2013-05-03 [National] False
2013-03-07 [Business, Financial Markets] True
2013-02-10 [Business, Economy] True