Python Enumerate Zip: Ready-to-Use Code Examples

These Python enumerate zip code snippets provide ready-to-use solutions for combining multiple sequences with index tracking. Copy and customize these examples for parallel iteration tasks.

Basic Enumerate Zip Patterns #

Simple Two-List Combination #

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Output:

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Usage: Perfect for combining related lists with position tracking.

Three-List Combination #

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Output:

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Usage: Inventory management, data correlation across multiple lists.

Data Processing with Enumerate Zip #

CSV-like Data Processing #

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Output:

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Usage: Database-like data processing, CSV file handling.

Time Series Data Alignment #

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Output:

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Usage: Weather data, sensor readings, time-based measurements.

Comparison and Analysis #

Before/After Comparison #

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Output:

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Usage: Performance tracking, A/B testing, progress monitoring.

Multi-Criteria Ranking #

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# Rank items based on multiple criteria
candidates = ['John', 'Sarah', 'Mike', 'Lisa']
technical_scores = [85, 92, 78, 88]
communication_scores = [78, 85, 90, 82]
experience_years = [5, 3, 7, 4]

print("Candidate Evaluation Report:")

weighted_scores = []
for pos, (name, tech, comm, exp) in enumerate(zip(candidates, technical_scores, 
                                                 communication_scores, experience_years), start=1):
    # Calculate weighted score (tech: 40%, comm: 30%, exp: 30%)
    weighted = (tech * 0.4) + (comm * 0.3) + (exp * 3)  # exp scaled to match score range
    weighted_scores.append((name, weighted, tech, comm, exp))
    
    print(f"Candidate {pos}: {name}")
    print(f"  Technical: {tech}/100")
    print(f"  Communication: {comm}/100")
    print(f"  Experience: {exp} years")
    print(f"  Weighted Score: {weighted:.1f}")
    print()

# Sort by weighted score and show ranking
ranked = sorted(weighted_scores, key=lambda x: x[1], reverse=True)
print("Final Ranking:")
for rank, (name, score, tech, comm, exp) in enumerate(ranked, start=1):
    print(f"{rank}. {name} (Score: {score:.1f})")

Output:

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Usage: Employee evaluation, product comparison, multi-factor analysis.

File and Data Processing #

Log File Analysis #

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Output:

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Usage: Log analysis, system monitoring, event processing.

Configuration Management #

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Output:

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Usage: Configuration management, settings validation, system setup.

Advanced Patterns #

Nested Enumerate Zip #

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Output:

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Usage: Hierarchical data processing, group analysis, nested structures.

Conditional Processing with Enumerate Zip #

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# Process data with conditional logic
products = ['Laptop', 'Mouse', 'Keyboard', 'Monitor', 'Webcam']
prices = [999, 25, 75, 300, 80]
stock_levels = [5, 150, 80, 12, 0]
categories = ['Electronics', 'Accessories', 'Accessories', 'Electronics', 'Accessories']

print("Inventory Status Report:")

low_stock_items = []
out_of_stock_items = []
high_value_items = []

for item_id, (product, price, stock, category) in enumerate(
    zip(products, prices, stock_levels, categories), start=1):
    
    # Determine status
    if stock == 0:
        status = "❌ OUT OF STOCK"
        out_of_stock_items.append(product)
    elif stock < 20:
        status = "⚠️ LOW STOCK"
        low_stock_items.append(product)
    else:
        status = "✅ IN STOCK"
    
    # Check if high value
    if price > 200:
        high_value_items.append(product)
        value_flag = " [HIGH VALUE]"
    else:
        value_flag = ""
    
    print(f"ID {item_id:02d}: {product} ({category})")
    print(f"        Price: ${price}, Stock: {stock} units")
    print(f"        Status: {status}{value_flag}")
    print()

# Summary
print("SUMMARY:")
print(f"Out of stock: {len(out_of_stock_items)} items")
print(f"Low stock: {len(low_stock_items)} items")
print(f"High value: {len(high_value_items)} items")

Output:

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Usage: Inventory management, conditional reporting, multi-criteria filtering.

Mathematical Operations #

Vector Operations #

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Output:

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Usage: Mathematical calculations, statistical analysis, scientific computing.

Financial Calculations #

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# Calculate financial metrics across time periods
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May']
revenue = [45000, 52000, 48000, 55000, 60000]
expenses = [35000, 38000, 36000, 42000, 45000]
targets = [50000, 50000, 50000, 55000, 55000]

print("Monthly Financial Analysis:")
print("=" * 60)

total_profit = 0
months_above_target = 0

for period, (month, rev, exp, target) in enumerate(zip(months, revenue, expenses, targets), start=1):
    profit = rev - exp
    profit_margin = (profit / rev) * 100
    target_achievement = (rev / target) * 100
    
    total_profit += profit
    if rev >= target:
        months_above_target += 1
        achievement_status = "✅ Target Met"
    else:
        achievement_status = "❌ Below Target"
    
    print(f"Period {period} - {month}:")
    print(f"  Revenue: ${rev:,}")
    print(f"  Expenses: ${exp:,}")
    print(f"  Profit: ${profit:,} ({profit_margin:.1f}% margin)")
    print(f"  Target: ${target:,} ({target_achievement:.1f}%) {achievement_status}")
    print()

average_profit = total_profit / len(months)
print(f"Summary:")
print(f"Total Profit: ${total_profit:,}")
print(f"Average Monthly Profit: ${average_profit:,.0f}")
print(f"Months Above Target: {months_above_target}/{len(months)}")

Output:

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Usage: Financial reporting, business analytics, performance tracking.

Quick Reference Templates #

Basic Template #

# Basic enumerate zip pattern
for index, (item1, item2) in enumerate(zip(list1, list2), start=1):
    print(f"{index}: {item1} - {item2}")

Three Lists Template #

# Three lists with enumerate
for i, (a, b, c) in enumerate(zip(list_a, list_b, list_c)):
    result = a + b + c  # Your operation here
    print(f"Item {i}: {result}")

Conditional Processing Template #

# Conditional processing with enumerate zip
for index, (item1, item2) in enumerate(zip(list1, list2), start=1):
    if condition(item1, item2):
        process_items(index, item1, item2)

Data Transformation Template #

# Transform data with enumerate zip
results = []
for i, (x, y) in enumerate(zip(data1, data2)):
    transformed = transform_function(x, y)
    results.append((i, transformed))

Performance Tips #

Memory Efficiency: zip() creates an iterator, not a list in memory
Length Handling: zip() stops at the shortest sequence
Error Handling: Check sequence lengths beforehand if needed
Type Safety: Ensure compatible data types across sequences

Common Use Cases #

Pattern	Use Case	Example
Two sequences	Data correlation	Names + Scores
Three+ sequences	Multi-attribute data	Product + Price + Stock
Time series	Temporal analysis	Time + Temperature + Humidity
Before/After	Change tracking	Old values + New values
Nested data	Hierarchical processing	Teams + Members + Scores

For more advanced iteration techniques, check out our Python iteration patterns and data processing guide.

Python Enumerate Zip: Ready-to-Use Code Examples

Python Enumerate Zip: Ready-to-Use Code Examples

Basic Enumerate Zip Patterns #

Simple Two-List Combination #

🐍 Try it yourself

Three-List Combination #

🐍 Try it yourself

Data Processing with Enumerate Zip #

CSV-like Data Processing #

🐍 Try it yourself

Time Series Data Alignment #

🐍 Try it yourself

Comparison and Analysis #

Before/After Comparison #

🐍 Try it yourself

Multi-Criteria Ranking #

🐍 Try it yourself

File and Data Processing #

Log File Analysis #

🐍 Try it yourself

Configuration Management #

🐍 Try it yourself

Advanced Patterns #

Nested Enumerate Zip #

🐍 Try it yourself

Conditional Processing with Enumerate Zip #

🐍 Try it yourself

Mathematical Operations #

Vector Operations #

🐍 Try it yourself

Financial Calculations #

🐍 Try it yourself

Quick Reference Templates #

Basic Template #

Three Lists Template #

Conditional Processing Template #

Data Transformation Template #

Performance Tips #

Common Use Cases #

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