pyspark如何修改Dataframe中一列的值

Question

数据值是这样的

Survived	age
0	22.0
1	38.0
1	26.0
1	35.0
0	35.0
0	null
0	54.0
0	2.0
1	27.0
1	14.0
1	4.0
1	58.0
0	20.0
0	39.0
0	14.0
1	55.0
0	2.0
1	null
0	31.0
1	null

age_interval = [(lower, upper) for lower, upper in zip(range(0, 96, 5), range(5, 101, 5))]
def age_partition(age):
    """ 将年龄分类 """
    for lower, upper in age_interval:
        if age is None:
            return "None"
        elif lower <= age <= upper:
            return f"({lower}, {upper})"

我想对age一列进行修改，比如把22.0改为(20, 30)，把38改成(30, 40)
上面的代码是对age值进行修改的函数

我应该如何对age列进行修改呢？

Answer 1

import pandas as pd
df = pd.read_csv('xxx.csv', header=0, encoding='utf-8')

age_interval = [(lower, upper) for lower, upper in zip(range(0, 96, 5), range(5, 101, 5))]
def age_partition(age):
    """ 将年龄分类 """
    for lower, upper in age_interval:
        if age is None:
            return "None"
        elif lower <= age <= upper:
            return f"({lower}, {upper})"

df['new_col'] = df.age.apply(age_partition)