package main import ( "fmt" "log" "github.com/datumbrain/otters" ) func main() { // Read CSV with automatic type inference df, err := otters.ReadCSV("sales.csv") if err != nil { log.Fatal(err) } // Chain operations like Pandas result := df. Filter("amount", ">", 1000). Select("region", "amount", "product"). Sort("amount", false) // descending if err := result.Error(); err != nil { log.Fatal(err) } // Get insights totalSales, _ := result.Sum("amount") avgDeal, _ := result.Mean("amount") fmt.Printf("Total sales: $%.2f\n", totalSales) fmt.Printf("Average deal: $%.2f\n", avgDeal) fmt.Printf("Top deals: %d\n", result.Count()) // Save results err = result.WriteCSV("top_sales.csv") if err != nil { log.Fatal(err) } }

// Load and explore data df, _ := otters.ReadCSV("employees.csv") // Basic info fmt.Println("Shape:", df.Shape()) // (1000, 5) fmt.Println("Columns:", df.Columns()) // [name, age, department, salary, hired_date] // Quick look fmt.Println(df.Head(5)) // First 5 rows fmt.Println(df.Tail(3)) // Last 3 rows fmt.Println(df.Describe()) // Summary statistics

// Multiple filters high_earners := df. Filter("salary", ">", 75000). Filter("department", "==", "Engineering"). Filter("age", "<=", 35) // Select specific columns summary := high_earners.Select("name", "salary", "age") // Complex conditions experienced := df.Filter("age", ">=", 30).Filter("salary", ">", 60000)

// Sort by single column top_paid := df.Sort("salary", false) // descending // Multi-column sort ranked := df.SortBy( []string{"department", "salary"}, []bool{true, false}, // department ascending, salary descending )

// Basic statistics avgSalary, _ := df.Mean("salary") totalPayroll, _ := df.Sum("salary") minSalary, _ := df.Min("salary") maxSalary, _ := df.Max("salary") stdDev, _ := df.Std("salary") fmt.Printf("Average salary: $%.2f\n", avgSalary) fmt.Printf("Total payroll: $%.2f\n", totalPayroll) fmt.Printf("Salary range: $%.2f - $%.2f\n", minSalary, maxSalary) fmt.Printf("Std deviation: $%.2f\n", stdDev) // Summary statistics for all numeric columns summary, _ := df.Describe() fmt.Println(summary)

// Create new columns df_with_bonus := df.Copy() // Add 10% bonus calculation (implementation coming soon) // Rename columns clean_df := df.RenameColumn("hired_date", "start_date") // Drop columns essential := df.Drop("internal_id", "notes")

// From CSV df, err := otters.ReadCSV("data.csv") df, err := otters.ReadCSVWithOptions("data.csv", otters.CSVOptions{ HasHeader: true, Delimiter: ',', SkipRows: 1, }) // From data df, err := otters.NewDataFrameFromMap(map[string]interface{}{ "name": []string{"Alice", "Bob", "Carol"}, "age": []int64{25, 30, 35}, "salary": []float64{50000, 60000, 70000}, })

// Filtering df.Filter("column", "==", value) // Equal df.Filter("column", "!=", value) // Not equal df.Filter("column", ">", value) // Greater than df.Filter("column", ">=", value) // Greater than or equal df.Filter("column", "<", value) // Less than df.Filter("column", "<=", value) // Less than or equal // Selection df.Select("col1", "col2", "col3") // Select columns df.Drop("col1", "col2") // Drop columns // Sorting df.Sort("column", true) // Single column, ascending df.Sort("column", false) // Single column, descending df.SortBy([]string{"col1", "col2"}, []bool{true, false})

// Basic stats df.Count() // Number of rows sum, _ := df.Sum("column") // Sum of numeric column mean, _ := df.Mean("column") // Average of numeric column min, _ := df.Min("column") // Minimum value max, _ := df.Max("column") // Maximum value std, _ := df.Std("column") // Standard deviation // Summary summary, _ := df.Describe() // Summary statistics for all numeric columns

// CSV df, err := otters.ReadCSV("input.csv") err = df.WriteCSV("output.csv") // With options df, err := otters.ReadCSVWithOptions("data.csv", otters.CSVOptions{ HasHeader: true, Delimiter: '\t', SkipRows: 2, MaxRows: 1000, })