How to categorize value using apply function and lambda-CodePudding

I'm trying to categorize an example table to 3 category using apply and lambda but the result is always mature, I dont get it so here is the code :

name=["rian","nancy","intan","rubim"]
age=["30","20","20","12"]
gender=["male","female","female","male"]
df = pd.DataFrame({
    "name":name,
    "age":age,
    "gender":gender
})

def category (age):
    if age<20:
        return 'kids'
    elif umur==20:
        return 'youth'
    else:
        return 'mature'

df['category']=df['age'].apply(age)

CodePudding user response：

There are several issues with your code:

you have to call apply with the function name, category
the category function references an undefined variable umur. Should this be age?
Your age data is of type string, but you want to compare it numerically.

Fixing these problems gives:

name = ["rian", "nancy", "intan", "rubim"]
age = [30, 20, 20, 12]
gender = ["male", "female", "female", "male"]
df = pd.DataFrame({
    "name": name,
    "age": age,
    "gender": gender
})

def category(age):
    if age < 20:
        return 'kids'
    elif age == 20:
        return 'youth'
    else:
        return 'mature'

df['category'] = df['age'].apply(category)

with the resulting DataFrame:

    name  age  gender category
0   rian   30    male   mature
1  nancy   20  female    youth
2  intan   20  female    youth
3  rubim   12    male     kids

I also suggest you to follow Python formatting conventions to make your code more readable.

CodePudding user response：

import pandas as pd
name = ["rian", "nancy", "intan", "rubim"]
age = ["30", "20", "20", "12"]
gender = ["male", "female", "female", "male"]
df = pd.DataFrame({
    "name": name,
    "age": age,
    "gender": gender
})

df.age = df.age.astype(int)
df['category'] = df['age'].apply(lambda x: 'mature' if x > 20 else 'kids' if x < 20 else 'youth')