Grouping & Aggregation

Learn how to group data and perform aggregations like count, sum, and average.

Count by Group

Use GroupBy() with Count() to count items in each group:

type Sale struct {
    Product  string
    Category string
    Amount   float64
    Quantity int
}

sales := []Sale{
    {"Laptop", "Electronics", 999.99, 2},
    {"Mouse", "Electronics", 29.99, 5},
    {"Desk", "Furniture", 299.99, 1},
    {"Chair", "Furniture", 199.99, 3},
    {"Keyboard", "Electronics", 79.99, 4},
}

result := plygo.From(sales).GroupBy("Category").Count()
for category, count := range result {
    fmt.Printf("%v: %d\n", category, count)
}

Result

Electronics: 3
Furniture: 2

Sum by Group

Use GroupBy() with Sum() to sum values in each group:

sales := []Sale{
    {"Laptop", "Electronics", 999.99, 2},
    {"Mouse", "Electronics", 29.99, 5},
    {"Desk", "Furniture", 299.99, 1},
    {"Chair", "Furniture", 199.99, 3},
    {"Keyboard", "Electronics", 79.99, 4},
}

result := plygo.From(sales).GroupBy("Category").Sum("Quantity")
for category, total := range result {
    fmt.Printf("%v: %.0f\n", category, total)
}

Result

Electronics: 11
Furniture: 4

Average by Group

Use GroupBy() with Avg() to calculate averages:

sales := []Sale{
    {"Laptop", "Electronics", 999.99, 2},
    {"Mouse", "Electronics", 29.99, 5},
    {"Desk", "Furniture", 299.99, 1},
    {"Chair", "Furniture", 199.99, 3},
    {"Keyboard", "Electronics", 79.99, 4},
}

result := plygo.From(sales).GroupBy("Category").Avg("Amount")
for category, avg := range result {
    fmt.Printf("%v: %.2f\n", category, avg)
}

Result

Electronics: 369.99
Furniture: 249.99

Available Aggregations

GroupBy supports these aggregation functions:

• Count() - Count items in each group
• Sum(field) - Sum numeric field values
• Avg(field) - Average of numeric field values
• Min(field) - Minimum value in each group
• Max(field) - Maximum value in each group

Next: Transformation