Python 如何使用pandas返回前10个常用列值？

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

日期

列的频率计数时，我得到

2011-01-01 650

。换句话说，

650

犯罪发生在整个数据集中的

2011-01-01

。但是，我想知道如何返回在

2011-01-01

中发生的

650

犯罪的前10个类别（

Category

列）。从中，我了解了索引选择和切片。尽管如此，我仍然不知道如何返回此类类别。

我认为这正是您想要的，首先使用

df.Dates==“2011-01-01”

构造一个逻辑索引，以筛选日期

2011-01-01

上的行，并在列索引处指定

Category

，以仅选择

Category

列，因此，您可以在

2011-01-01

上获得所有类别。使用

value\u counts（）

函数为每个类别创建一个频率表，并按默认情况下升序的频率进行排序，为了获得最频繁的类别，您可以使用列表

[：-1]

反向索引来反向频率计数并使用

[：10]

选择前10个元素，它们将是前十大最常见元素的类别：

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

---

显示您所做的操作。请使用

print df

或同等工具提供您的原始

DataFrame

，我无法正确加载您的

df

。感谢您的帮助，这已奏效。你能解释一下吗？。