Practical Usage Patterns
Common Array Operations
Iterating Through Arrays
numbers := [5]int{10, 20, 30, 40, 50}
for index, value := range numbers {
fmt.Printf("Index: %d, Value: %d\n", index, value)
}
Array Comparison
func compareArrays(arr1 [5]int, arr2 [5]int) bool {
return arr1 == arr2
}
Data Processing Patterns
Matrix Operations
matrix := [3][3]int{
{1, 2, 3},
{4, 5, 6},
{7, 8, 9}
}
func sumMatrixDiagonal(m [3][3]int) int {
sum := 0
for i := 0; i < 3; i++ {
sum += m[i][i]
}
return sum
}
graph TD
A[Array Transformation] --> B[Filtering]
A --> C[Mapping]
A --> D[Reducing]
Array Manipulation Strategies
Strategy |
Description |
Use Case |
Filtering |
Remove unwanted elements |
Data cleaning |
Mapping |
Transform elements |
Data conversion |
Reducing |
Aggregate values |
Statistical analysis |
Advanced Usage Scenarios
Parallel Processing
func processArray(data [10]int) []int {
result := make([]int, len(data))
for i, v := range data {
result[i] = v * 2
}
return result
}
Memory-Efficient Techniques
// Avoiding unnecessary copies
func processLargeArray(arr [1000]int) {
// Efficient processing without full array copy
}
- Use array pointers for large datasets
- Minimize unnecessary iterations
- Leverage compile-time optimizations
Real-world Applications on LabEx
- Scientific computing
- Data analysis
- Algorithm implementation
Error Handling and Validation
func validateArray(arr [5]int) error {
for _, value := range arr {
if value < 0 {
return fmt.Errorf("negative value found")
}
}
return nil
}
Best Practices
- Choose appropriate array size
- Use type-specific initialization
- Consider memory implications
- Validate input data
By understanding these practical usage patterns, developers can effectively leverage arrays in Go, creating efficient and robust code solutions across various domains.