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Conditionals in Generator Expressions – Memory-Efficient Filtering 2026

Adding conditionals to generator expressions combines the power of filtering with lazy evaluation. This pattern is extremely useful in data science when you need to process large datasets while keeping memory usage minimal.

TL;DR — Two Ways to Add Conditions

Filtering: (expr for item in iterable if condition)
Conditional Value: (expr_if_true if condition else expr_if_false for item in iterable)

1. Basic Filtering with if

scores = [85, 92, 78, 95, 88, 76, 91, 65]

# Generator that yields only high scores
high_scores = (score for score in scores if score >= 90)

for score in high_scores:
    print(score)

2. Conditional Expressions (if-else) in Generators

scores = [85, 92, 78, 95, 65, 45]

# Categorize on-the-fly without creating a full list
categories = (
    "High" if s >= 90 
    else "Medium" if s >= 75 
    else "Low" 
    for s in scores
)

for cat in categories:
    print(cat)

3. Real-World Data Science Examples

import pandas as pd

df = pd.read_csv("large_sales_data.csv")

# Example 1: Memory-efficient high-value transaction filter
high_value_revenue = sum(
    row.amount * 1.1 
    for row in df.itertuples() 
    if row.amount > 1500 and row.region == "North"
)

# Example 2: Lazy feature engineering with conditions
processed_amounts = (
    0 if amount <= 0 
    else round(amount ** 0.5, 2) 
    for amount in df["amount"] 
    if pd.notna(amount)
)

# Example 3: Streaming customer categorization
premium_customers = (
    row.customer_id 
    for row in df.itertuples() 
    if row.amount > 2000 
    and row.region in ("North", "West")
)

for cust_id in premium_customers:
    print(f"Premium customer: {cust_id}")

4. Best Practices in 2026

Use filtering if at the end for selecting rows
Use if-else inside for value transformation
Combine with sum(), max(), or a single for loop to keep memory low
Avoid deeply nested conditionals — they reduce readability
For very complex logic, use a traditional generator function with yield

Conclusion

Conditionals in generator expressions give you the best of both worlds: clean, readable filtering/transformation combined with excellent memory efficiency. In 2026 data science workflows, this pattern is preferred when processing large files, streaming data, or building memory-conscious pipelines.

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