python - See if item in each row of pandas series -


i have pandas series following data:

2015-07-24                            ['business', 'corporate'] 2015-07-24                            ['business', 'corporate'] 2015-07-08                              ['commentary', 'world'] 2015-07-05                          ['issues', 'just 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 , 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 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

is there way boolean array telling me whether each list contains either "business" or "food & drink"?

using 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

look @ results side side

df.assign(i=df.tags.apply(set) & set(['business', 'food & drink']))                                                      tags      2015-07-24                         [business, corporate]   true 2015-07-24                         [business, corporate]   true 2015-07-08                           [commentary, world]  false 2015-07-05                       [issues, 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 , 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 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

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