Zip Lists¶

Zero-dependency Python snippets for zipping multiple lists using the standard library.

10 snippets available in this sub-category.

Simple¶

Zip two lists¶

list zip combine pairs data-structures

Zip two lists together

def zip_lists(list1, list2):
    """Zip two lists together."""
    return list(zip(list1, list2))


names = ["Alice", "Bob", "Charlie"]
ages = [25, 30, 35]
result = zip_lists(names, ages)
print(result)  # [('Alice', 25), ('Bob', 30), ('Charlie', 35)]

Notes

Uses built-in zip function
Creates tuples of paired elements
Stops at shortest list length
Simple and efficient

Zip multiple lists¶

list zip multiple combine data-structures

Zip multiple lists together

def zip_multiple_lists(*lists):
    """Zip multiple lists together."""
    return list(zip(*lists))


names = ["Alice", "Bob", "Charlie"]
ages = [25, 30, 35]
cities = ["NYC", "LA", "Chicago"]
result = zip_multiple_lists(names, ages, cities)
print(result)  # [('Alice', 25, 'NYC'), ('Bob', 30, 'LA'), ('Charlie', 35, 'Chicago')]

Notes

Variable number of lists
Uses unpacking operator
Flexible input
Handles any number of lists

Complex¶

Zip lists with different lengths¶

list zip different lengths fill data-structures

Zip lists with different lengths using fill value

def zip_lists_different_lengths(*lists, fill_value=None):
    """Zip lists with different lengths using fill value."""
    max_length = max(len(lst) for lst in lists)
    padded_lists = []

    for lst in lists:
        # Pad shorter lists with fill value
        padded = lst + [fill_value] * (max_length - len(lst))
        padded_lists.append(padded)

    return list(zip(*padded_lists))


names = ["Alice", "Bob", "Charlie"]
ages = [25, 30]
cities = ["NYC", "LA", "Chicago", "Boston"]
result = zip_lists_different_lengths(names, ages, cities, fill_value="N/A")
print(
    result
)  # [('Alice', 25, 'NYC'), ('Bob', 30, 'LA'), ('Charlie', 'N/A', 'Chicago'), ('N/A', 'N/A', 'Boston')]

Notes

Handles unequal lengths
Configurable fill value
Pads shorter lists
Useful for data alignment

Zip lists with custom pairing function¶

list zip custom function pairing data-structures

Zip lists with custom pairing function

def zip_lists_custom(*lists, pair_func=None):
    """Zip lists with custom pairing function."""
    if pair_func is None:
        def pair_func(*args):
            return args

    zipped = zip(*lists)
    return [pair_func(*pair) for pair in zipped]


names = ["Alice", "Bob", "Charlie"]
ages = [25, 30, 35]


# Custom pairing: create dictionary
def create_person_dict(name, age):
    return {"name": name, "age": age}


result = zip_lists_custom(names, ages, pair_func=create_person_dict)
print(
    result
)  # [{'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 30}, {'name': 'Charlie', 'age': 35}]


# Custom pairing: create formatted string
def format_person(name, age):
    return f"{name} is {age} years old"


result2 = zip_lists_custom(names, ages, pair_func=format_person)
print(result2)  # ['Alice is 25 years old', 'Bob is 30 years old', 'Charlie is 35 years old']

Notes

Flexible pairing logic
Custom data structures
Formatted output
Advanced use cases

Zip lists with condition¶

list zip conditional filter data-structures

Zip lists with conditional filtering

def zip_lists_conditional(*lists, condition_func=None):
    """Zip lists with conditional filtering."""
    if condition_func is None:
        def condition_func(*args):
            True

    zipped = zip(*lists)
    return [pair for pair in zipped if condition_func(*pair)]


names = ["Alice", "Bob", "Charlie", "David"]
ages = [25, 30, 35, 20]
cities = ["NYC", "LA", "Chicago", "Boston"]


# Filter by age > 25
def age_filter(name, age, city):
    return age > 25


result = zip_lists_conditional(names, ages, cities, condition_func=age_filter)
print(result)  # [('Bob', 30, 'LA'), ('Charlie', 35, 'Chicago')]


# Filter by city starting with 'C'
def city_filter(name, age, city):
    return city.startswith("C")


result2 = zip_lists_conditional(names, ages, cities, condition_func=city_filter)
print(result2)  # [('Charlie', 35, 'Chicago')]

Notes

Conditional pairing
Flexible filtering
Multiple criteria
Data filtering

Zip lists with index¶

list zip index enumerate data-structures

Zip lists with index information

def zip_lists_with_index(*lists):
    """Zip lists with their indices."""
    return list(enumerate(zip(*lists)))


names = ["Alice", "Bob", "Charlie"]
ages = [25, 30, 35]
result = zip_lists_with_index(names, ages)
print(result)  # [(0, ('Alice', 25)), (1, ('Bob', 30)), (2, ('Charlie', 35))]


# Alternative: include index in each tuple
def zip_lists_with_index_inline(*lists):
    """Zip lists with index included in each tuple."""
    return [(i, *pair) for i, pair in enumerate(zip(*lists))]


result2 = zip_lists_with_index_inline(names, ages)
print(result2)  # [(0, 'Alice', 25), (1, 'Bob', 30), (2, 'Charlie', 35)]

Notes

Includes position information
Useful for tracking
Two indexing styles
Position-aware processing

Zip lists with generator¶

list zip generator memory optimization data-structures

Zip lists using generator

def zip_lists_generator(*lists):
    """Zip lists using generator for memory efficiency."""
    return zip(*lists)


names = ["Alice", "Bob", "Charlie"]
ages = [25, 30, 35]
cities = ["NYC", "LA", "Chicago"]

# Memory efficient iteration
for name, age, city in zip_lists_generator(names, ages, cities):
    print(f"{name} ({age}) from {city}")

# Convert to list if needed
result = list(zip_lists_generator(names, ages, cities))
print(result)  # [('Alice', 25, 'NYC'), ('Bob', 30, 'LA'), ('Charlie', 35, 'Chicago')]

Notes

Memory efficient
Lazy evaluation
Suitable for large lists
Generator pattern

Zip lists with error handling¶

list zip safe error handling data-structures

Safely zip lists with error handling

def zip_lists_different_lengths(*lists, fill_value=None):
    # Function is defined in one of the above code block
    pass


def zip_lists_safe(*lists, fill_value=None):
    """Safely zip lists with error handling."""
    try:
        if not lists:
            return []

        # Validate all inputs are lists
        for i, lst in enumerate(lists):
            if not isinstance(lst, list):
                raise TypeError(f"Argument {i} must be a list, got {type(lst).__name__}")

        if fill_value is not None:
            return zip_lists_different_lengths(*lists, fill_value=fill_value)
        else:
            return list(zip(*lists))

    except Exception as e:
        print(f"Error zipping lists: {e}")
        return []  # Return empty list on error


# Safe zipping with error handling
try:
    result = zip_lists_safe(["Alice", "Bob"], [25, 30], fill_value="N/A")
    print(result)  # [('Alice', 25), ('Bob', 30)]
except Exception as e:
    print(f"Error: {e}")

Notes

Comprehensive error handling
Type validation
Graceful degradation
Production ready

Zip lists with performance monitoring¶

list zip performance timing monitoring data-structures

Zip lists with performance monitoring

import time


def zip_lists_generator(*lists):
    # Function is defined in one of the above code block
    pass


def zip_lists_different_lengths(*lists, fill_value=None):
    # Function is defined in one of the above code block
    pass


def zip_lists_with_timing(*lists, method="standard"):
    """Zip lists with performance monitoring."""
    start_time = time.time()

    if method == "standard":
        result = list(zip(*lists))
    elif method == "generator":
        result = list(zip_lists_generator(*lists))
    elif method == "fill_value":
        result = zip_lists_different_lengths(*lists, fill_value=None)
    else:
        raise ValueError("Method must be 'standard', 'generator', or 'fill_value'")

    end_time = time.time()

    return {
        "result": result,
        "execution_time": end_time - start_time,
        "num_lists": len(lists),
        "total_elements": sum(len(lst) for lst in lists),
        "method": method,
    }


# Performance comparison
large_list1 = list(range(10000))
large_list2 = list(range(10000))
large_list3 = list(range(10000))

std_stats = zip_lists_with_timing(large_list1, large_list2, large_list3, method="standard")
gen_stats = zip_lists_with_timing(large_list1, large_list2, large_list3, method="generator")

print(f"Standard method: {std_stats['execution_time']:.6f}s")
print(f"Generator method: {gen_stats['execution_time']:.6f}s")

Notes

Performance measurement
Method comparison
Benchmarking tool
Optimization insights

Zip lists with custom aggregation¶

list zip aggregate function custom data-structures

Zip lists with custom aggregation

def zip_lists_aggregate(*lists, aggregate_func=None):
    """Zip lists with custom aggregation function."""
    if aggregate_func is None:
        def aggregate_func(*args):
            return args


    zipped = zip(*lists)
    aggregated = []

    for pair in zipped:
        result = aggregate_func(*pair)
        aggregated.append(result)

    return aggregated


numbers1 = [1, 2, 3, 4]
numbers2 = [5, 6, 7, 8]
numbers3 = [9, 10, 11, 12]


# Sum aggregation
def sum_aggregate(a, b, c):
    return a + b + c


result = zip_lists_aggregate(numbers1, numbers2, numbers3, aggregate_func=sum_aggregate)
print(result)  # [15, 18, 21, 24]


# Average aggregation
def avg_aggregate(a, b, c):
    return (a + b + c) / 3


result2 = zip_lists_aggregate(numbers1, numbers2, numbers3, aggregate_func=avg_aggregate)
print(result2)  # [5.0, 6.0, 7.0, 8.0]

Notes

Custom aggregation logic
Mathematical operations
Statistical calculations
Data processing

🔗 Cross-References¶

Reference: See 📂 Flatten List
Reference: See 📂 Remove Duplicates From List
Reference: See 📂 Sort List
Reference: See 📂 Rotate List

🏷️ Tags¶

list, zip, combine, generator, performance, aggregation, data-structures

📝 Notes¶

Built-in zip function is most efficient for standard use cases
Generators provide memory efficiency for large lists
Custom pairing functions enable complex data transformations
Fill values handle lists of different lengths
Conditional zipping is useful for data filtering
Consider performance implications for very large lists