Advanced-Custom-Lambda

Sat 17 May 2025

title: "Advanced Custom Lambda" author: "Rj" date: 2019-04-22 description: "-" type: technical_note draft: false

import numpy as np
import pandas as pd

# Check the student passed either math or passed at least in 2 subjects. If no condidition matched, consider them as failed
def pass_math_or_two_subjects(row):
    if(row.maths > 34):
        return 'Pass'
    if(row.language > 34 and row.science > 34):
        return 'Pass'
    return 'Fail'

df = pd.read_csv('abc.csv')

df

	student	language	science	maths	history
0	kumar	90	56	34	34
1	kevin	10	34	32	67
2	sammy	90	23	12	32
3	janice	20	67	90	45
4	peter	30	56	45	65
5	prem	90	45	45	34
6	carrol	50	90	45	23

df['special_result'] = df.apply(pass_math_or_two_subjects, axis=1)

df

	student	language	science	maths	history	special_result
0	kumar	90	56	34	34	Pass
1	kevin	10	34	32	67	Fail
2	sammy	90	23	12	32	Fail
3	janice	20	67	90	45	Pass
4	peter	30	56	45	65	Pass
5	prem	90	45	45	34	Pass
6	carrol	50	90	45	23	Pass

# Filter those candidates whoever passed the given criteria
df_passed = df[df.apply(pass_math_or_two_subjects, axis=1) == 'Pass']

df_passed

	student	language	science	maths	history	special_result
0	kumar	90	56	34	34	Pass
3	janice	20	67	90	45	Pass
4	peter	30	56	45	65	Pass
5	prem	90	45	45	34	Pass
6	carrol	50	90	45	23	Pass

Score: 5

Category: data-wrangling