从PANDAS系列打印出一串值

Question

我在下面有以下代码：

census_df = pd.read_csv('/Users/xyz/census.csv')

def answer_six():
        df = census_df.groupby(["STNAME"]).apply(lambda x: x.sort_values(["CENSUS2010POP"], ascending=False)).reset_index(
            drop=True)
        df.groupby('STNAME').head(3)[['STNAME', 'CENSUS2010POP']]
        values = df.sort_values(['CENSUS2010POP'], ascending=False).head(3)[['STNAME']]
        values = values['STNAME']
        return values

我正在尝试执行以下操作：

仅查看人口最多的三个州 每个州，什么是[[人口最多的三个州]]（按人口从高到低的顺序排列）？ 我的函数应返回

字符串值列表。

但是，它目前正在返回熊猫系列。

任何人都可以帮我吗？

这里是原始数据的样本：

SUMLEV,REGION,DIVISION,STATE,COUNTY,STNAME,CTYNAME,CENSUS2010POP,ESTIMATESBASE2010,POPESTIMATE2010,POPESTIMATE2011,POPESTIMATE2012,POPESTIMATE2013,POPESTIMATE2014,POPESTIMATE2015,NPOPCHG_2010,NPOPCHG_2011,NPOPCHG_2012,NPOPCHG_2013,NPOPCHG_2014,NPOPCHG_2015,BIRTHS2010,BIRTHS2011,BIRTHS2012,BIRTHS2013,BIRTHS2014,BIRTHS2015,DEATHS2010,DEATHS2011,DEATHS2012,DEATHS2013,DEATHS2014,DEATHS2015,NATURALINC2010,NATURALINC2011,NATURALINC2012,NATURALINC2013,NATURALINC2014,NATURALINC2015,INTERNATIONALMIG2010,INTERNATIONALMIG2011,INTERNATIONALMIG2012,INTERNATIONALMIG2013,INTERNATIONALMIG2014,INTERNATIONALMIG2015,DOMESTICMIG2010,DOMESTICMIG2011,DOMESTICMIG2012,DOMESTICMIG2013,DOMESTICMIG2014,DOMESTICMIG2015,NETMIG2010,NETMIG2011,NETMIG2012,NETMIG2013,NETMIG2014,NETMIG2015,RESIDUAL2010,RESIDUAL2011,RESIDUAL2012,RESIDUAL2013,RESIDUAL2014,RESIDUAL2015,GQESTIMATESBASE2010,GQESTIMATES2010,GQESTIMATES2011,GQESTIMATES2012,GQESTIMATES2013,GQESTIMATES2014,GQESTIMATES2015,RBIRTH2011,RBIRTH2012,RBIRTH2013,RBIRTH2014,RBIRTH2015,RDEATH2011,RDEATH2012,RDEATH2013,RDEATH2014,RDEATH2015,RNATURALINC2011,RNATURALINC2012,RNATURALINC2013,RNATURALINC2014,RNATURALINC2015,RINTERNATIONALMIG2011,RINTERNATIONALMIG2012,RINTERNATIONALMIG2013,RINTERNATIONALMIG2014,RINTERNATIONALMIG2015,RDOMESTICMIG2011,RDOMESTICMIG2012,RDOMESTICMIG2013,RDOMESTICMIG2014,RDOMESTICMIG2015,RNETMIG2011,RNETMIG2012,RNETMIG2013,RNETMIG2014,RNETMIG2015
040,3,6,01,000,Alabama,Alabama,4779736,4780127,4785161,4801108,4816089,4830533,4846411,4858979,5034,15947,14981,14444,15878,12568,14226,59689,59062,57938,58334,58305,11089,48811,48357,50843,50228,50330,3137,10878,10705,7095,8106,7975,1357,4926,4904,4834,5529,5726,537,11,-929,1838,2816,-2268,1894,4937,3975,6672,8345,3458,3,132,301,677,-573,1135,116185,116212,115560,115666,116963,119088,119599,12.453020044,12.282580881,12.012080498,12.056285538,12.014973123,10.183523955,10.056360497,10.541099257,10.380963246,10.371556424,2.2694960886,2.2262203842,1.4709812409,1.6753222918,1.6434166994,1.0277199607,1.0198397724,1.0022161125,1.1427161302,1.1799628866,0.0022949492,-0.193195585,0.3810660353,0.5820019213,-0.467369163,1.0300149099,0.8266441875,1.3832821479,1.7247180515,0.7125937237
050,3,6,01,001,Alabama,Autauga County,54571,54571,54660,55253,55175,55038,55290,55347,89,593,-78,-137,252,57,151,636,615,574,623,600,152,507,558,583,504,467,-1,129,57,-9,119,133,33,20,16,16,18,19,49,398,-161,-166,125,-140,82,418,-145,-150,143,-121,8,46,10,22,-10,45,455,455,455,455,455,455,455,11.572789388,11.138479371,10.416194097,11.293597274,10.846281081,9.2254783329,10.106132503,10.579514213,9.1363933,8.4420221083,2.3473110551,1.0323468685,-0.163320117,2.1572039736,2.404258973,0.3639241946,0.2897815771,0.2903468738,0.3262997607,0.3434655676,7.2420914723,-2.91592712,-3.012348815,2.2659705605,-2.530798919,7.6060156669,-2.626145543,-2.722001942,2.5922703212,-2.187333351

具有预期输出的数据帧示例（我希望这是字符串列表）：

        STNAME
190   California
2566       Texas
1860    New York

我下面有以下代码：census_df = pd.read_csv（'/ Users / xyz / census.csv'）def answer_six（）：df = census_df.groupby（[“ STNAME”]）。apply（lambda x：x .sort_values（[“ CENSUS2010POP”]，...

Answer 1

def answer_six():
    highest_counties = highest_counties.groupby('STNAME')['CENSUS2010POP'].sum()
    largest = highest_counties.nlargest(3)
    largest_statenames = largest.index.values.tolist()
    return largest_statenames