Python 如何在pandas中定义用户定义的函数

我有一个csv文件，其中包含以下信息

name    salary  department
a        2500      x
b        5000      y
c        10000      y
d        20000      x 

我需要使用Pandas将其转换为如下形式

dept    name    position
x        a       Normal Employee
x        b       Normal Employee
y        c       Experienced Employee
y        d       Experienced Employee

---

如果工资为8000&&您可以定义两个掩码并将其传递给

np。其中

：

In [91]:
normal = df['salary'] <= 8000
experienced = (df['salary'] > 8000) & (df['salary'] <= 25000)
df['position'] = np.where(normal, 'normal emplyee', np.where(experienced, 'experienced employee', 'unknown'))
df

Out[91]:
  name  salary department              position
0    a    2500          x        normal emplyee
1    b    5000          y        normal emplyee
2    c   10000          y  experienced employee
3    d   20000          x  experienced employee

---

一个有用的函数是

apply

：

data_df['position'] = data_df['salary'].apply(lambda salary: 'Normal Employee' if salary <= 8000 else 'Experienced Employee', axis=1)

---

data_-df['position']=data_-df['salary']。应用（lambda-salary:'Normal Employee'，如果salary我将使用一个简单的函数，如：

def f(x):
    if x <= 8000:
        x = 'Normal Employee'
    elif 8000 < x <= 25000:
        x = 'Experienced Employee'
    return x

apply
对于大df来说会很慢
apply
对于大df来说会很慢当我们使用groupby时如何获得正常员工的计数你能解释一下你的意思吗？你是在使用
df.groupby（'position'）.count（）吗？
data_df['position'] = data_df['salary'].apply(lambda salary: 'Normal Employee' if salary <= 8000 else 'Experienced Employee', axis=1)

def f(x):
    if x <= 8000:
        x = 'Normal Employee'
    elif 8000 < x <= 25000:
        x = 'Experienced Employee'
    return x

df['position'] = df['salary'].apply(f)