Golang에서 CSV 파일 작업 방법

Go的encoding/csv包可轻松处理CSV文件读写操作。1. 读取CSV文件时，使用os.Open打开文件并用csv.NewReader解析，可通过ReadAll()一次性读取或Read()逐行读取以节省内存；2. 写入CSV文件时，使用os.Create创建文件并用csv.NewWriter写入数据，需调用Flush()确保数据写入，或使用WriteAll()批量写入；3. 处理带标题的结构化数据时，可手动将记录映射到struct，如跳过首行标题并解析字段，或使用第三方库实现自动绑定；4. 对于包含逗号、换行或引号的字段，csv包会自动处理转义，必要时可通过设置Comma字段更改编码分隔符。Go的csv包已覆盖大多数场景，结合外部库可实现更高级功能。

Working with CSV files in Go is straightforward thanks to the built-in encoding/csv package. Whether you're reading from or writing to CSV files, Go provides clean and efficient tools. Here's how to handle common CSV operations.

Reading CSV files

To read a CSV file, you open the file and wrap a csv.Reader around it. The reader parses each line into a slice of strings.

package main

import (
    "encoding/csv"
    "fmt"
    "os"
)

func main() {
    file, err := os.Open("data.csv")
    if err != nil {
        fmt.Println("Error opening file:", err)
        return
    }
    defer file.Close()

    reader := csv.NewReader(file)
    records, err := reader.ReadAll()
    if err != nil {
        fmt.Println("Error reading CSV:", err)
        return
    }

    for _, record := range records {
        fmt.Println(record) // Each record is a []string
    }
}

• Use Read() instead of ReadAll() if you want to process one line at a time, which is more memory-efficient for large files.
• The first row is treated like any other unless you handle it separately (e.g., as headers).

Writing CSV files

Writing to a CSV file is just as simple. You create a csv.Writer and feed it slices of strings.

package main

import (
    "encoding/csv"
    "os"
)

func main() {
    file, err := os.Create("output.csv")
    if err != nil {
        fmt.Println("Error creating file:", err)
        return
    }
    defer file.Close()

    writer := csv.NewWriter(file)
    defer writer.Flush()

    data := [][]string{
        {"Name", "Age", "City"},
        {"Alice", "30", "New York"},
        {"Bob", "25", "Los Angeles"},
    }

    for _, record := range data {
        if err := writer.Write(record); err != nil {
            fmt.Println("Error writing record:", err)
            return
        }
    }
}

• Always call writer.Flush() to ensure all data is written to the file.
• You can write all at once using writer.WriteAll(data) if you have the full dataset ready.

Handling CSV with headers (struct mapping)

For structured data, it’s common to map CSV rows to structs. While Go’s standard library doesn’t do this automatically, you can combine Read() with manual parsing.

type Person struct {
    Name string
    Age  int
    City string
}

file, _ := os.Open("people.csv")
defer file.Close()

reader := csv.NewReader(file)
records, _ := reader.ReadAll()

var people []Person

for i, record := range records {
    if i == 0 {
        continue // Skip header
    }
    age, _ := strconv.Atoi(record[1])
    people = append(people, Person{
        Name: record[0],
        Age:  age,
        City: record[2],
    })
}

• This approach gives you control but requires manual field mapping.
• For automatic struct tagging, consider third-party libraries like github.com/gocarina/gocsv or github.com/jszwec/csvutil.

Dealing with commas and quotes

CSV files often contain fields with commas, newlines, or quotes. The csv.Reader and csv.Writer handle these automatically by default.

• Fields like "Smith, John" are correctly parsed as a single field.
• The writer will quote fields when necessary (e.g., if they contain commas or newlines).

If you need to customize the delimiter (e.g., use semicolons), change the Comma field:

reader := csv.NewReader(file)
reader.Comma = ';' // For semicolon-separated files

Same applies to csv.Writer.

Basically, Go’s encoding/csv package covers most use cases. Read line by line for large files, use structs with manual mapping for typed data, and let the package handle escaping. For more advanced scenarios like automatic struct binding, external libraries can save time.

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