Python 使用Pandas GroupBy和value_计数查找最常见的值
我正在处理表中的两列Python 使用Pandas GroupBy和value_计数查找最常见的值,python,python-3.x,pandas,pandas-groupby,Python,Python 3.x,Pandas,Pandas Groupby,我正在处理表中的两列 +-------------+--------------------------------------------------------------+ | Area Name | Code Description | +-------------+--------------------------------------------------------------+ |
+-------------+--------------------------------------------------------------+
| Area Name | Code Description |
+-------------+--------------------------------------------------------------+
| N Hollywood | VIOLATION OF RESTRAINING ORDER |
| N Hollywood | CRIMINAL THREATS - NO WEAPON DISPLAYED |
| N Hollywood | CRIMINAL THREATS - NO WEAPON DISPLAYED |
| N Hollywood | ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT |
| Southeast | ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT |
| West Valley | CRIMINAL THREATS - NO WEAPON DISPLAYED |
| West Valley | CRIMINAL THREATS - NO WEAPON DISPLAYED |
| 77th Street | RAPE, FORCIBLE |
| Foothill | CRM AGNST CHLD (13 OR UNDER) (14-15 & SUSP 10 YRS OLDER)0060 |
| N Hollywood | VANDALISM - FELONY ($400 & OVER, ALL CHURCH VANDALISMS) 0114 |
+-------------+--------------------------------------------------------------+
我使用Groupby和value_计数按区域名称查找代码描述
df.groupby(['Area Name'])['Code Description'].value_counts()
+---------------------------------------------------------------------------------+
| Wilshire SHOPLIFTING-GRAND THEFT ($950.01 & OVER) 7 |
+---------------------------------------------------------------------------------+
是否有办法仅查看每个区域名称的前“n”个值?如果我将.nlargest(3)
附加到上面的代码,它只返回一个区域名称的结果
df.groupby(['Area Name'])['Code Description'].value_counts()
+---------------------------------------------------------------------------------+
| Wilshire SHOPLIFTING-GRAND THEFT ($950.01 & OVER) 7 |
+---------------------------------------------------------------------------------+
根据
值\u计数的结果,在每组中使用头部
:
df.groupby('Area Name')['Code Description'].apply(lambda x: x.value_counts().head(3))
输出:
Area Name
77th Street RAPE, FORCIBLE 1
Foothill CRM AGNST CHLD (13 OR UNDER) (14-15 & SUSP 10 YRS OLDER)0060 1
N Hollywood CRIMINAL THREATS - NO WEAPON DISPLAYED 2
VIOLATION OF RESTRAINING ORDER 1
ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT 1
Southeast ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT 1
West Valley CRIMINAL THREATS - NO WEAPON DISPLAYED 2
Name: Code Description, dtype: int64
您可以执行双groupby
:
s = df.groupby('Area Name')['Code Description'].value_counts()
res = s.groupby('Area Name').nlargest(3).reset_index(level=1, drop=True)
print(res)
Area Name Code Description
77th Street RAPE, FORCIBLE 1
Foothill CRM AGNST CHLD (13 OR UNDER) (14-15 & SUSP 10 YRS OLDER)0060 1
N Hollywood CRIMINAL THREATS - NO WEAPON DISPLAYED 2
ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT 1
VANDALISM - FELONY ($400 & OVER, ALL CHURCH VANDALISMS) 0114 1
Southeast ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT 1
West Valley CRIMINAL THREATS - NO WEAPON DISPLAYED 2
Name: Code Description, dtype: int64
我的问题更清楚了。问题被重新打开了,因为这里是count TOP N值,在另一个问题中是最常见的值,所以这里不能使用这个问题的答案。