Efficient Table Creation
Choosing the Right Data Structure
Dictionary-Based Lookup Tables
## Fast key-value lookup
country_codes = {
'USA': '+1',
'UK': '+44',
'France': '+33'
}
List-Based Lookup Tables
## Index-based lookup
fibonacci = [0, 1, 1, 2, 3, 5, 8, 13, 21]
Generation Techniques
Comprehension Methods
## List comprehension
squares = {x: x**2 for x in range(10)}
## Generator-based creation
def create_power_table(base, limit):
return {x: base**x for x in range(limit)}
Method |
Time Complexity |
Memory Efficiency |
Dictionary |
O(1) |
Moderate |
List |
O(1) |
Low |
Numpy Array |
O(1) |
High |
Advanced Creation Strategies
flowchart TD
A[Lookup Table Creation] --> B[Comprehensions]
A --> C[Generator Functions]
A --> D[Numpy Generation]
A --> E[External Data Sources]
Numpy-Based Efficient Tables
import numpy as np
## High-performance numeric lookup
def create_numpy_lookup(start, end, step):
return np.arange(start, end, step)
Dynamic Table Generation
def generate_multiplication_table(max_num):
return {
(x, y): x * y
for x in range(1, max_num + 1)
for y in range(1, max_num + 1)
}
LabEx Optimization Tips
- Prefer dictionary comprehensions
- Use generator expressions
- Leverage numpy for numeric tables
- Minimize redundant computations
Memory-Efficient Techniques
## Lazy evaluation with generators
def lazy_lookup_table(limit):
return (x**2 for x in range(limit))
Error Handling and Validation
def safe_lookup_table(data_dict, default=None):
return lambda key: data_dict.get(key, default)
Practical Considerations
- Choose structure based on access pattern
- Consider memory constraints
- Validate performance with profiling
- Implement caching mechanisms