Multi-Condition Vectorization with np.select() in Pandas

Question

Given customer tenure in months and spend, categorize customers into: 'VIP' (spend > 100k), 'Loyal' (tenure > 24), 'New High Growth' (spend > 25k and tenure <= 6), and 'Standard' otherwise.

Accepted Answer

Optimal Solution:
import numpy as np
import pandas as pd

conditions = [
    df['spend'] > 100000,
    df['tenure'] > 24,
    (df['spend'] > 25000) & (df['tenure'] <= 6)
]

choices = ['VIP', 'Loyal', 'New High Growth']

# np.select evaluates conditions in order, defaulting to 'Standard'
df['segment'] = np.select(conditions, choices, default='Standard')

result_df = df[['name', 'segment']]

Technical Explanation:
`np.select(conditions, choices, default=...)` is the vectorized Python equivalent of SQL `CASE WHEN`. It executes at compiled C speed without nested loops.

name	spend	tenure
Aarav	120000	12
Bhavna	15000	36
Chirag	30000	3
Divya	10000	10

name	segment
Aarav	VIP
Bhavna	Loyal
Chirag	New High Growth
Divya	Standard