使用 Pandas GroupBy 和 value_counts 查找最常见的值答案

【问题标题】：Finding most common values with Pandas GroupBy and value_counts使用 Pandas GroupBy 和 value_counts 查找最常见的值
【发布时间】：2018-11-08 14:31:34
【问题描述】：

我正在处理表格中的两列。

+-------------+--------------------------------------------------------------+
|  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 |
+-------------+--------------------------------------------------------------+

我正在使用 Groupby 和 value_counts 按区域名称查找代码描述。

df.groupby(['Area Name'])['Code Description'].value_counts()

有没有办法只查看每个区域名称的前“n”个值？如果我将.nlargest(3) 附加到上面的代码中，它只会返回一个区域名称的结果。

+---------------------------------------------------------------------------------+
| Wilshire     SHOPLIFTING-GRAND THEFT ($950.01 & OVER)                         7 |
+---------------------------------------------------------------------------------+

【问题讨论】：

我的问题更清楚了。
问题被重新打开，因为这里是count TOP N个值，在另一个问题中最常见的值，所以这里不能使用这个答案。

标签： python python-3.x pandas pandas-groupby

【解决方案1】：

根据value_counts的结果在每组中使用head：

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

【讨论】：

【解决方案2】：

你可以执行双重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

【讨论】：