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Programming language: Go
Tags: Serialization    
Latest version: v1.4.0

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README

csvutil PkgGoDev GoDoc Build Status Build status Go Report Card codecov

Package csvutil provides fast and idiomatic mapping between CSV and Go (golang) values.

This package does not provide a CSV parser itself, it is based on the Reader and Writer interfaces which are implemented by eg. std Go (golang) csv package. This gives a possibility of choosing any other CSV writer or reader which may be more performant.

Installation

go get github.com/jszwec/csvutil

Requirements

  • Go1.7+

Index

  1. Examples
    1. Unmarshal
    2. Marshal
    3. Unmarshal and metadata
    4. But my CSV file has no header...
    5. Decoder.Map - data normalization
    6. Different separator/delimiter
    7. Decoder and interface values
    8. Custom time.Time format
    9. Custom struct tags
    10. Slice and Map fields
    11. Nested/Embedded structs
    12. Inline tag
  2. Performance
    1. Unmarshal
    2. Marshal

Example

Unmarshal

Nice and easy Unmarshal is using the Go std csv.Reader with its default options. Use Decoder for streaming and more advanced use cases.

    var csvInput = []byte(`
name,age,CreatedAt
jacek,26,2012-04-01T15:00:00Z
john,,0001-01-01T00:00:00Z`,
    )

    type User struct {
        Name      string `csv:"name"`
        Age       int    `csv:"age,omitempty"`
        CreatedAt time.Time
    }

    var users []User
    if err := csvutil.Unmarshal(csvInput, &users); err != nil {
        fmt.Println("error:", err)
    }

    for _, u := range users {
        fmt.Printf("%+v\n", u)
    }

    // Output:
    // {Name:jacek Age:26 CreatedAt:2012-04-01 15:00:00 +0000 UTC}
    // {Name:john Age:0 CreatedAt:0001-01-01 00:00:00 +0000 UTC}

Marshal

Marshal is using the Go std csv.Writer with its default options. Use Encoder for streaming or to use a different Writer.

    type Address struct {
        City    string
        Country string
    }

    type User struct {
        Name string
        Address
        Age       int `csv:"age,omitempty"`
        CreatedAt time.Time
    }

    users := []User{
        {
            Name:      "John",
            Address:   Address{"Boston", "USA"},
            Age:       26,
            CreatedAt: time.Date(2010, 6, 2, 12, 0, 0, 0, time.UTC),
        },
        {
            Name:    "Alice",
            Address: Address{"SF", "USA"},
        },
    }

    b, err := csvutil.Marshal(users)
    if err != nil {
        fmt.Println("error:", err)
    }
    fmt.Println(string(b))

    // Output:
    // Name,City,Country,age,CreatedAt
    // John,Boston,USA,26,2010-06-02T12:00:00Z
    // Alice,SF,USA,,0001-01-01T00:00:00Z

Unmarshal and metadata

It may happen that your CSV input will not always have the same header. In addition to your base fields you may get extra metadata that you would still like to store. Decoder provides Unused method, which after each call to Decode can report which header indexes were not used during decoding. Based on that, it is possible to handle and store all these extra values.

    type User struct {
        Name      string            `csv:"name"`
        City      string            `csv:"city"`
        Age       int               `csv:"age"`
        OtherData map[string]string `csv:"-"`
    }

    csvReader := csv.NewReader(strings.NewReader(`
name,age,city,zip
alice,25,la,90005
bob,30,ny,10005`))

    dec, err := csvutil.NewDecoder(csvReader)
    if err != nil {
        log.Fatal(err)
    }

    header := dec.Header()
    var users []User
    for {
        u := User{OtherData: make(map[string]string)}

        if err := dec.Decode(&u); err == io.EOF {
            break
        } else if err != nil {
            log.Fatal(err)
        }

        for _, i := range dec.Unused() {
            u.OtherData[header[i]] = dec.Record()[i]
        }
        users = append(users, u)
    }

    fmt.Println(users)

    // Output:
    // [{alice la 25 map[zip:90005]} {bob ny 30 map[zip:10005]}]

But my CSV file has no header...

Some CSV files have no header, but if you know how it should look like, it is possible to define a struct and generate it. All that is left to do, is to pass it to a decoder.

    type User struct {
        ID   int
        Name string
        Age  int `csv:",omitempty"`
        City string
    }

    csvReader := csv.NewReader(strings.NewReader(`
1,John,27,la
2,Bob,,ny`))

    // in real application this should be done once in init function.
    userHeader, err := csvutil.Header(User{}, "csv")
    if err != nil {
        log.Fatal(err)
    }

    dec, err := csvutil.NewDecoder(csvReader, userHeader...)
    if err != nil {
        log.Fatal(err)
    }

    var users []User
    for {
        var u User
        if err := dec.Decode(&u); err == io.EOF {
            break
        } else if err != nil {
            log.Fatal(err)
        }
        users = append(users, u)
    }

    fmt.Printf("%+v", users)

    // Output:
    // [{ID:1 Name:John Age:27 City:la} {ID:2 Name:Bob Age:0 City:ny}]

Decoder.Map - data normalization

The Decoder's Map function is a powerful tool that can help clean up or normalize the incoming data before the actual decoding takes place.

Lets say we want to decode some floats and the csv input contains some NaN values, but these values are represented by the 'n/a' string. An attempt to decode 'n/a' into float will end up with error, because strconv.ParseFloat expects 'NaN'. Knowing that, we can implement a Map function that will normalize our 'n/a' string and turn it to 'NaN' only for float types.

    dec, err := NewDecoder(r)
    if err != nil {
        log.Fatal(err)
    }

    dec.Map = func(field, column string, v interface{}) string {
        if _, ok := v.(float64); ok && field == "n/a" {
            return "NaN"
        }
        return field
    }

Now our float64 fields will be decoded properly into NaN. What about float32, float type aliases and other NaN formats? Look at the full example here.

Different separator/delimiter

Some files may use different value separators, for example TSV files would use \t. The following examples show how to set up a Decoder and Encoder for such use case.

Decoder:

    csvReader := csv.NewReader(r)
    csvReader.Comma = '\t'

    dec, err := NewDecoder(csvReader)
    if err != nil {
        log.Fatal(err)
    }

    var users []User
    for {
        var u User
        if err := dec.Decode(&u); err == io.EOF {
            break
        } else if err != nil {
            log.Fatal(err)
        }
        users = append(users, u)
    }

Encoder:

    var buf bytes.Buffer

    w := csv.NewWriter(&buf)
        w.Comma = '\t'
    enc := csvutil.NewEncoder(w)

    for _, u := range users {
        if err := enc.Encode(u); err != nil {
            log.Fatal(err)
        }
        }

    w.Flush()
    if err := w.Error(); err != nil {
        log.Fatal(err)
    }

Decoder and interface values

In the case of interface struct fields data is decoded into strings. However, if Decoder finds out that these fields were initialized with pointer values of a specific type prior to decoding, it will try to decode data into that type.

Why only pointer values? Because these values must be both addressable and settable, otherwise Decoder will have to initialize these types on its own, which could result in losing some unexported information.

If interface stores a non-pointer value it will be replaced with a string.

This example will show how this feature could be useful:

package main

import (
    "bytes"
    "encoding/csv"
    "fmt"
    "io"
    "log"

    "github.com/jszwec/csvutil"
)

// Value defines one record in the csv input. In this example it is important
// that Type field is defined before Value. Decoder reads headers and values
// in the same order as struct fields are defined.
type Value struct {
    Type  string      `csv:"type"`
    Value interface{} `csv:"value"`
}

func main() {
    // lets say our csv input defines variables with their types and values.
    data := []byte(`
type,value
string,string_value
int,10
`)

    dec, err := csvutil.NewDecoder(csv.NewReader(bytes.NewReader(data)))
    if err != nil {
        log.Fatal(err)
    }

    // we would like to read every variable and store their already parsed values
    // in the interface field. We can use Decoder.Map function to initialize
    // interface with proper values depending on the input.
    var value Value
    dec.Map = func(field, column string, v interface{}) string {
        if column == "type" {
            switch field {
            case "int": // csv input tells us that this variable contains an int.
                var n int
                value.Value = &n // lets initialize interface with an initialized int pointer.
            default:
                return field
            }
        }
        return field
    }

    for {
        value = Value{}
        if err := dec.Decode(&value); err == io.EOF {
            break
        } else if err != nil {
            log.Fatal(err)
        }

        if value.Type == "int" {
            // our variable type is int, Map func already initialized our interface
            // as int pointer, so we can safely cast it and use it.
            n, ok := value.Value.(*int)
            if !ok {
                log.Fatal("expected value to be *int")
            }
            fmt.Printf("value_type: %s; value: (%T) %d\n", value.Type, value.Value, *n)
        } else {
            fmt.Printf("value_type: %s; value: (%T) %v\n", value.Type, value.Value, value.Value)
        }
    }

    // Output:
    // value_type: string; value: (string) string_value
    // value_type: int; value: (*int) 10
}

Custom time.Time format

Type time.Time can be used as is in the struct fields by both Decoder and Encoder due to the fact that both have builtin support for encoding.TextUnmarshaler and encoding.TextMarshaler. This means that by default Time has a specific format; look at MarshalText and UnmarshalText. There are two ways to override it, which one you choose depends on your use case:

  1. Via Register func (based on encoding/json) ```go const format = "2006/01/02 15:04:05"

marshalTime := func(t time.Time) ([]byte, error) { return t.AppendFormat(nil, format), nil }

unmarshalTime := func(data []byte, t *time.Time) error { tt, err := time.Parse(format, string(data)) if err != nil { return err } *t = tt return nil }

enc := csvutil.NewEncoder(w) enc.Register(marshalTime)

dec, err := csvutil.NewDecoder(r) if err != nil { return err } dec.Register(unmarshalTime)


2. With custom type:
```go
type Time struct {
    time.Time
}

const format = "2006/01/02 15:04:05"

func (t Time) MarshalCSV() ([]byte, error) {
    var b [len(format)]byte
    return t.AppendFormat(b[:0], format), nil
}

func (t *Time) UnmarshalCSV(data []byte) error {
    tt, err := time.Parse(format, string(data))
    if err != nil {
        return err
    }
    *t = Time{Time: tt}
    return nil
}

Custom struct tags

Like in other Go encoding packages struct field tags can be used to set custom names or options. By default encoders and decoders are looking at csv tag. However, this can be overriden by manually setting the Tag field.

    type Foo struct {
        Bar int `custom:"bar"`
    }
    dec, err := csvutil.NewDecoder(r)
    if err != nil {
        log.Fatal(err)
    }
    dec.Tag = "custom"
    enc := csvutil.NewEncoder(w)
    enc.Tag = "custom"

Slice and Map fields

There is no default encoding/decoding support for slice and map fields because there is no CSV spec for such values. In such case, it is recommended to create a custom type alias and implement Marshaler and Unmarshaler interfaces. Please note that slice and map aliases behave differently than aliases of other types - there is no need for type casting.

    type Strings []string

    func (s Strings) MarshalCSV() ([]byte, error) {
        return []byte(strings.Join(s, ",")), nil // strings.Join takes []string but it will also accept Strings
    }

    type StringMap map[string]string

    func (sm StringMap) MarshalCSV() ([]byte, error) {
        return []byte(fmt.Sprint(sm)), nil
    }

    func main() {
        b, err := csvutil.Marshal([]struct {
            Strings Strings   `csv:"strings"`
            Map     StringMap `csv:"map"`
        }{
            {[]string{"a", "b"}, map[string]string{"a": "1"}}, // no type casting is required for slice and map aliases
            {Strings{"c", "d"}, StringMap{"b": "1"}},
        })

        if err != nil {
            log.Fatal(err)
        }

        fmt.Printf("%s\n", b)

        // Output:
        // strings,map
        // "a,b",map[a:1]
        // "c,d",map[b:1]
    }

Nested/Embedded structs

Both Encoder and Decoder support nested or embedded structs.

Playground: https://play.golang.org/p/ZySjdVkovbf

package main

import (
    "fmt"

    "github.com/jszwec/csvutil"
)

type Address struct {
    Street string `csv:"street"`
    City   string `csv:"city"`
}

type User struct {
    Name string `csv:"name"`
    Address
}

func main() {
    users := []User{
        {
            Name: "John",
            Address: Address{
                Street: "Boylston",
                City:   "Boston",
            },
        },
    }

    b, err := csvutil.Marshal(users)
    if err != nil {
        panic(err)
    }

    fmt.Printf("%s\n", b)

    var out []User
    if err := csvutil.Unmarshal(b, &out); err != nil {
        panic(err)
    }

    fmt.Printf("%+v\n", out)

    // Output:
    //
    // name,street,city
    // John,Boylston,Boston
    //
    // [{Name:John Address:{Street:Boylston City:Boston}}]
}

Inline tag

Fields with inline tag behave similarly to embedded struct fields. However, it gives a possibility to specify the prefix for all underlying fields. This can be useful when one structure can define multiple CSV columns because they are different from each other only by a certain prefix. Look at the example below.

Playground: https://play.golang.org/p/jyEzeskSnj7

package main

import (
    "fmt"

    "github.com/jszwec/csvutil"
)

func main() {
    type Address struct {
        Street string `csv:"street"`
        City   string `csv:"city"`
    }

    type User struct {
        Name        string  `csv:"name"`
        Address     Address `csv:",inline"`
        HomeAddress Address `csv:"home_address_,inline"`
        WorkAddress Address `csv:"work_address_,inline"`
        Age         int     `csv:"age,omitempty"`
    }

    users := []User{
        {
            Name:        "John",
            Address:     Address{"Washington", "Boston"},
            HomeAddress: Address{"Boylston", "Boston"},
            WorkAddress: Address{"River St", "Cambridge"},
            Age:         26,
        },
    }

    b, err := csvutil.Marshal(users)
    if err != nil {
        fmt.Println("error:", err)
    }

    fmt.Printf("%s\n", b)

    // Output:
    // name,street,city,home_address_street,home_address_city,work_address_street,work_address_city,age
    // John,Washington,Boston,Boylston,Boston,River St,Cambridge,26
}

Performance

csvutil provides the best encoding and decoding performance with small memory usage.

Unmarshal

benchmark code

csvutil:

BenchmarkUnmarshal/csvutil.Unmarshal/1_record-12              280696          4516 ns/op        7332 B/op         26 allocs/op
BenchmarkUnmarshal/csvutil.Unmarshal/10_records-12             95750         11517 ns/op        8356 B/op         35 allocs/op
BenchmarkUnmarshal/csvutil.Unmarshal/100_records-12            14997         83146 ns/op       18532 B/op        125 allocs/op
BenchmarkUnmarshal/csvutil.Unmarshal/1000_records-12            1485        750143 ns/op      121094 B/op       1025 allocs/op
BenchmarkUnmarshal/csvutil.Unmarshal/10000_records-12            154       7587205 ns/op     1136662 B/op      10025 allocs/op
BenchmarkUnmarshal/csvutil.Unmarshal/100000_records-12            14      76126616 ns/op    11808744 B/op     100025 allocs/op

gocsv:

BenchmarkUnmarshal/gocsv.Unmarshal/1_record-12                141330          7499 ns/op        7795 B/op         97 allocs/op
BenchmarkUnmarshal/gocsv.Unmarshal/10_records-12               54252         21664 ns/op       13891 B/op        307 allocs/op
BenchmarkUnmarshal/gocsv.Unmarshal/100_records-12               6920        159662 ns/op       72644 B/op       2380 allocs/op
BenchmarkUnmarshal/gocsv.Unmarshal/1000_records-12               752       1556083 ns/op      650248 B/op      23083 allocs/op
BenchmarkUnmarshal/gocsv.Unmarshal/10000_records-12               72      17086623 ns/op     7017469 B/op     230092 allocs/op
BenchmarkUnmarshal/gocsv.Unmarshal/100000_records-12               7     163610749 ns/op    75004923 B/op    2300105 allocs/op

easycsv:

BenchmarkUnmarshal/easycsv.ReadAll/1_record-12                101527         10662 ns/op        8855 B/op         81 allocs/op
BenchmarkUnmarshal/easycsv.ReadAll/10_records-12               23325         51437 ns/op       24072 B/op        391 allocs/op
BenchmarkUnmarshal/easycsv.ReadAll/100_records-12               2402        447296 ns/op      170538 B/op       3454 allocs/op
BenchmarkUnmarshal/easycsv.ReadAll/1000_records-12               272       4370854 ns/op     1595683 B/op      34057 allocs/op
BenchmarkUnmarshal/easycsv.ReadAll/10000_records-12               24      47502457 ns/op    18861808 B/op     340068 allocs/op
BenchmarkUnmarshal/easycsv.ReadAll/100000_records-12               3     468974170 ns/op    189427066 B/op   3400082 allocs/op

Marshal

benchmark code

csvutil:

BenchmarkMarshal/csvutil.Marshal/1_record-12              279558          4390 ns/op        9952 B/op         12 allocs/op
BenchmarkMarshal/csvutil.Marshal/10_records-12             82478         15608 ns/op       10800 B/op         21 allocs/op
BenchmarkMarshal/csvutil.Marshal/100_records-12            10275        117288 ns/op       28208 B/op        112 allocs/op
BenchmarkMarshal/csvutil.Marshal/1000_records-12            1075       1147473 ns/op      168508 B/op       1014 allocs/op
BenchmarkMarshal/csvutil.Marshal/10000_records-12            100      11985382 ns/op     1525973 B/op      10017 allocs/op
BenchmarkMarshal/csvutil.Marshal/100000_records-12             9     113640813 ns/op    22455873 B/op     100021 allocs/op

gocsv:

BenchmarkMarshal/gocsv.Marshal/1_record-12                203052          6077 ns/op        5914 B/op         81 allocs/op
BenchmarkMarshal/gocsv.Marshal/10_records-12               50132         24585 ns/op        9284 B/op        360 allocs/op
BenchmarkMarshal/gocsv.Marshal/100_records-12               5480        212008 ns/op       51916 B/op       3151 allocs/op
BenchmarkMarshal/gocsv.Marshal/1000_records-12               514       2053919 ns/op      444506 B/op      31053 allocs/op
BenchmarkMarshal/gocsv.Marshal/10000_records-12               52      21066666 ns/op     4332377 B/op     310064 allocs/op
BenchmarkMarshal/gocsv.Marshal/100000_records-12               5     207408929 ns/op    51169419 B/op    3100077 allocs/op