Python 在列中对集合类型中的值进行计数

在图像上有一个如下所示的数据帧

df = pd.DataFrame({'bus':[{268},{23,200,268},{24},{24},{200,268}],
'problem':["Driver Issues" ,"Driver Issues" , "Timing Problem","Routing",
"Timing Problem"]})

“总线”列表示总线号，“问题”列包含有关总线的投诉。在总线列中，任何行都可以有一个或多个总线号

我试图数一数每一辆公交车的车号，以及最常见的问题/问题/投诉。。查找最常见的公交车号码及其最常见的投诉

但由于集合类型的原因，无法使用计数器func

输出可以如下所示：

df2 = pd.DataFrame({'busses':["268","24","200","23"],
'ComplainFrequency':["3" ,"2" , "2","1"]})

及

---

首先将集合展平到新的

数据帧

：

df1 = pd.DataFrame([(c, b) for a, b in zip(df['bus'], df['problem']) for c in a], 
                    columns=['bus','problem'])
print (df1)
   bus         problem
0  268   Driver Issues
1  200   Driver Issues
2  268   Driver Issues
3   23   Driver Issues
4   24  Timing Problem
5   24         Routing
6  200  Timing Problem
7  268  Timing Problem

如果存在带有

，

的字符串值集，则需要进行双重展平：

df = pd.DataFrame({'bus':[{'268'},{'23,200,268'},{'24'},{'24'},{'200,268'}], 
                   'problem':["Driver Issues" ,"Driver Issues" , "Timing Problem",
                              "Routing","Timing Problem"]})

print (df)
            bus         problem
0         {268}   Driver Issues
1  {23,200,268}   Driver Issues
2          {24}  Timing Problem
3          {24}         Routing
4     {200,268}  Timing Problem

df1 = pd.DataFrame([(d, b) for a, b in zip(df['bus'], df['problem']) 
                           for c in a 
                           for d in c.split(',')], 
                    columns=['bus','problem'])

print (df1)
   bus         problem
0  268   Driver Issues
1   23   Driver Issues
2  200   Driver Issues
3  268   Driver Issues
4   24  Timing Problem
5   24         Routing
6  200  Timing Problem
7  268  Timing Problem

然后使用：

---

我编辑了帖子，而不是图片和链接添加您的数据帧和所需的输出光。我编辑帖子是为了澄清问题，您能再看一遍吗help@nous-请现在检查。

df = pd.DataFrame({'bus':[{'268'},{'23,200,268'},{'24'},{'24'},{'200,268'}], 
                   'problem':["Driver Issues" ,"Driver Issues" , "Timing Problem",
                              "Routing","Timing Problem"]})

print (df)
            bus         problem
0         {268}   Driver Issues
1  {23,200,268}   Driver Issues
2          {24}  Timing Problem
3          {24}         Routing
4     {200,268}  Timing Problem

df1 = pd.DataFrame([(d, b) for a, b in zip(df['bus'], df['problem']) 
                           for c in a 
                           for d in c.split(',')], 
                    columns=['bus','problem'])

print (df1)
   bus         problem
0  268   Driver Issues
1   23   Driver Issues
2  200   Driver Issues
3  268   Driver Issues
4   24  Timing Problem
5   24         Routing
6  200  Timing Problem
7  268  Timing Problem

df2 = df1.groupby('bus')['problem'].size().reset_index(name='ComplainFrequency')
print (df2)
   bus  ComplainFrequency
0  200                  2
1   23                  1
2   24                  2
3  268                  3

df3 = df1.groupby(['bus','problem']).size().reset_index(name='Coplains')
print (df3)
   bus         problem  Coplains
0  200   Driver Issues         1
1  200  Timing Problem         1
2   23   Driver Issues         1
3   24         Routing         1
4   24  Timing Problem         1
5  268   Driver Issues         2
6  268  Timing Problem         1