如何使用pandas返回前10个常用列值？

Question

我在玩一个著名的犯罪数据集。看起来是这样的：

Dates,Category,Descript,DayOfWeek,PdDistrict,Resolution,Address,X,Y,Time
2015-05-13,VANDALISM,"MALICIOUS MISCHIEF, VANDALISM OF VEHICLES",Wednesday,TENDERLOIN,NONE,TURK ST / JONES ST,-122.41241426358101,37.7830037964534,22:30:00
2015-05-13,VANDALISM,"MALICIOUS MISCHIEF, VANDALISM",Wednesday,NORTHERN,NONE,1500 Block of FILLMORE ST,-122.432743822617,37.7838424505847,20:45:00
2015-05-13,VANDALISM,"MALICIOUS MISCHIEF, VANDALISM",Wednesday,NORTHERN,NONE,1100 Block of FILLMORE ST,-122.431979576386,37.7800478529923,17:07:00
2015-05-13,VANDALISM,"MALICIOUS MISCHIEF, VANDALISM OF VEHICLES",Wednesday,TENDERLOIN,NONE,LEAVENWORTH ST / EDDY ST,-122.414242955907,37.783724025447796,17:00:00
2015-05-13,VANDALISM,"MALICIOUS MISCHIEF, VANDALISM OF VEHICLES",Wednesday,CENTRAL,NONE,CALIFORNIA ST / STOCKTON ST,-122.40753977435699,37.79224917725779,16:45:00
2015-05-13,VANDALISM,"MALICIOUS MISCHIEF, VANDALISM",Wednesday,BAYVIEW,NONE,100 Block of KISKA RD,-122.375989158092,37.7301576924252,16:00:00
2015-05-13,VANDALISM,"MALICIOUS MISCHIEF, VANDALISM OF VEHICLES",Wednesday,NORTHERN,"ARREST, BOOKED",300 Block of MCALLISTER ST,-122.417777932619,37.7803089893403,14:30:00
2015-05-13,NON-CRIMINAL,LOST PROPERTY,Wednesday,TENDERLOIN,NONE,300 Block of OFARRELL ST,-122.41050925879499,37.786043222299206,21:00:00
2015-05-13,LARCENY/THEFT,GRAND THEFT FROM LOCKED AUTO,Wednesday,NORTHERN,NONE,2000 Block of BUSH ST,-122.43101755702699,37.7873880712241,21:00:00
.....
2015-05-13,LARCENY/THEFT,GRAND THEFT FROM LOCKED AUTO,Wednesday,INGLESIDE,NONE,500 Block of COLLEGE AV,-122.42365634294501,37.7325564882065,21:00:00
2015-05-13,LARCENY/THEFT,ATTEMPTED THEFT FROM LOCKED VEHICLE,Wednesday,TARAVAL,NONE,19TH AV / SANTIAGO 

当我得到Dates列的频率计数时，我得到2011-01-01 650。换句话说，在整个数据集中，{}犯罪发生在{}。但是，我想知道如何返回发生在2011-01-01的650犯罪的前10个类别（Category列）。从documentation我读到了关于索引选择和切片的内容。然而，我仍然不知道如何返回这样的类别。你知道吗

Answer 1

我想这正是您想要的，首先用df.Dates == "2011-01-01"构造一个逻辑索引来过滤日期2011-01-01上的行，并在列索引处指定Category来只选择Category列，这样您就得到了2011-01-01上的所有类别。使用value_counts()函数为每个类别创建一个频率表，并按频率进行排序（默认情况下是升序），为了获得最频繁的类别，您可以使用list [::-1]反向索引来反转频率计数，并使用[:10]来选取前10个元素，这些元素将是前10个最频繁的类别频繁：

df.loc[df.Dates == "2011-01-01", "Category"].value_counts().sort_values()[::-1][:10]