10 Popular Golang Libraries for Validation

In today's post we will learn about 10 Popular Golang Libraries for Validation. 

What is Validation?

Validation is a simple concept to understand but difficult to put into practice.

Validation is the recognition and acceptance of another persons internal experience as being valid. Emotional validation is distinguished from emotional invalidation, in which your own or another persons emotional experiences are rejected, ignored, or judged. Self-validation is the recognition and acknowledgement of your own internal experience.

Validation does not mean agreeing with or supporting feelings or thoughts. Validating does not mean love. You can validate someone you don’t like even though you probably wouldn’t want to.

Table of contents:

  • Validator - Go Struct and Field validation, including Cross Field, Cross Struct, Map, Slice and Array diving.
  • Gody - 🎈 A lightweight struct validator for Go.
  • Govalid - Fast, tag-based validation for structs.
  • Govalidator - Validators and sanitizers for strings, numerics, slices and structs.
  • Govalidator - Validate Golang request data with simple rules. Highly inspired by Laravel's request validation.
  • Jio - Jio is a json schema validator similar to joi.
  • Ozzo-validation - Supports validation of various data types (structs, strings, maps, slices, etc.) with configurable and extensible validation rules specified in usual code constructs instead of struct tags.
  • Terraform-validator - A norms and conventions validator for Terraform.
  • Validate - Go package for data validation and filtering. support validate Map, Struct, Request(Form, JSON, url.Values, Uploaded Files) data and more features.
  • Validate - This package provides a framework for writing validations for Go applications.

1 - Validator:

Go Struct and Field validation, including Cross Field, Cross Struct, Map, Slice and Array diving.

Package validator implements value validations for structs and individual fields based on tags.

It has the following unique features:

  • Cross Field and Cross Struct validations by using validation tags or custom validators.
  • Slice, Array and Map diving, which allows any or all levels of a multidimensional field to be validated.
  • Ability to dive into both map keys and values for validation
  • Handles type interface by determining it's underlying type prior to validation.
  • Handles custom field types such as sql driver Valuer see Valuer
  • Alias validation tags, which allows for mapping of several validations to a single tag for easier defining of validations on structs
  • Extraction of custom defined Field Name e.g. can specify to extract the JSON name while validating and have it available in the resulting FieldError
  • Customizable i18n aware error messages.
  • Default validator for the gin web framework; upgrading from v8 to v9 in gin see here

Installation

Use go get.

go get github.com/go-playground/validator/v10

Then import the validator package into your own code.

import "github.com/go-playground/validator/v10"

Error Return Value

Validation functions return type error

They return type error to avoid the issue discussed in the following, where err is always != nil:

Validator returns only InvalidValidationError for bad validation input, nil or ValidationErrors as type error; so, in your code all you need to do is check if the error returned is not nil, and if it's not check if error is InvalidValidationError ( if necessary, most of the time it isn't ) type cast it to type ValidationErrors like so:

err := validate.Struct(mystruct)
validationErrors := err.(validator.ValidationErrors)

Usage and documentation

Please see https://pkg.go.dev/github.com/go-playground/validator/v10 for detailed usage docs.

Benchmarks

Run on MacBook Pro (15-inch, 2017) go version go1.10.2 darwin/amd64

goos: darwin
goarch: amd64
pkg: github.com/go-playground/validator
BenchmarkFieldSuccess-8                                         20000000                83.6 ns/op             0 B/op          0 allocs/op
BenchmarkFieldSuccessParallel-8                                 50000000                26.8 ns/op             0 B/op          0 allocs/op
BenchmarkFieldFailure-8                                          5000000               291 ns/op             208 B/op          4 allocs/op
BenchmarkFieldFailureParallel-8                                 20000000               107 ns/op             208 B/op          4 allocs/op
BenchmarkFieldArrayDiveSuccess-8                                 2000000               623 ns/op             201 B/op         11 allocs/op
BenchmarkFieldArrayDiveSuccessParallel-8                        10000000               237 ns/op             201 B/op         11 allocs/op
BenchmarkFieldArrayDiveFailure-8                                 2000000               859 ns/op             412 B/op         16 allocs/op
BenchmarkFieldArrayDiveFailureParallel-8                         5000000               335 ns/op             413 B/op         16 allocs/op
BenchmarkFieldMapDiveSuccess-8                                   1000000              1292 ns/op             432 B/op         18 allocs/op
BenchmarkFieldMapDiveSuccessParallel-8                           3000000               467 ns/op             432 B/op         18 allocs/op
BenchmarkFieldMapDiveFailure-8                                   1000000              1082 ns/op             512 B/op         16 allocs/op
BenchmarkFieldMapDiveFailureParallel-8                           5000000               425 ns/op             512 B/op         16 allocs/op
BenchmarkFieldMapDiveWithKeysSuccess-8                           1000000              1539 ns/op             480 B/op         21 allocs/op
BenchmarkFieldMapDiveWithKeysSuccessParallel-8                   3000000               613 ns/op             480 B/op         21 allocs/op
BenchmarkFieldMapDiveWithKeysFailure-8                           1000000              1413 ns/op             721 B/op         21 allocs/op
BenchmarkFieldMapDiveWithKeysFailureParallel-8                   3000000               575 ns/op             721 B/op         21 allocs/op
BenchmarkFieldCustomTypeSuccess-8                               10000000               216 ns/op              32 B/op          2 allocs/op
BenchmarkFieldCustomTypeSuccessParallel-8                       20000000                82.2 ns/op            32 B/op          2 allocs/op
BenchmarkFieldCustomTypeFailure-8                                5000000               274 ns/op             208 B/op          4 allocs/op
BenchmarkFieldCustomTypeFailureParallel-8                       20000000               116 ns/op             208 B/op          4 allocs/op
BenchmarkFieldOrTagSuccess-8                                     2000000               740 ns/op              16 B/op          1 allocs/op
BenchmarkFieldOrTagSuccessParallel-8                             3000000               474 ns/op              16 B/op          1 allocs/op
BenchmarkFieldOrTagFailure-8                                     3000000               471 ns/op             224 B/op          5 allocs/op
BenchmarkFieldOrTagFailureParallel-8                             3000000               414 ns/op             224 B/op          5 allocs/op
BenchmarkStructLevelValidationSuccess-8                         10000000               213 ns/op              32 B/op          2 allocs/op
BenchmarkStructLevelValidationSuccessParallel-8                 20000000                91.8 ns/op            32 B/op          2 allocs/op
BenchmarkStructLevelValidationFailure-8                          3000000               473 ns/op             304 B/op          8 allocs/op
BenchmarkStructLevelValidationFailureParallel-8                 10000000               234 ns/op             304 B/op          8 allocs/op
BenchmarkStructSimpleCustomTypeSuccess-8                         5000000               385 ns/op              32 B/op          2 allocs/op
BenchmarkStructSimpleCustomTypeSuccessParallel-8                10000000               161 ns/op              32 B/op          2 allocs/op
BenchmarkStructSimpleCustomTypeFailure-8                         2000000               640 ns/op             424 B/op          9 allocs/op
BenchmarkStructSimpleCustomTypeFailureParallel-8                 5000000               318 ns/op             440 B/op         10 allocs/op
BenchmarkStructFilteredSuccess-8                                 2000000               597 ns/op             288 B/op          9 allocs/op
BenchmarkStructFilteredSuccessParallel-8                        10000000               266 ns/op             288 B/op          9 allocs/op
BenchmarkStructFilteredFailure-8                                 3000000               454 ns/op             256 B/op          7 allocs/op
BenchmarkStructFilteredFailureParallel-8                        10000000               214 ns/op             256 B/op          7 allocs/op
BenchmarkStructPartialSuccess-8                                  3000000               502 ns/op             256 B/op          6 allocs/op
BenchmarkStructPartialSuccessParallel-8                         10000000               225 ns/op             256 B/op          6 allocs/op
BenchmarkStructPartialFailure-8                                  2000000               702 ns/op             480 B/op         11 allocs/op
BenchmarkStructPartialFailureParallel-8                          5000000               329 ns/op             480 B/op         11 allocs/op
BenchmarkStructExceptSuccess-8                                   2000000               793 ns/op             496 B/op         12 allocs/op
BenchmarkStructExceptSuccessParallel-8                          10000000               193 ns/op             240 B/op          5 allocs/op
BenchmarkStructExceptFailure-8                                   2000000               639 ns/op             464 B/op         10 allocs/op
BenchmarkStructExceptFailureParallel-8                           5000000               300 ns/op             464 B/op         10 allocs/op
BenchmarkStructSimpleCrossFieldSuccess-8                         3000000               417 ns/op              72 B/op          3 allocs/op
BenchmarkStructSimpleCrossFieldSuccessParallel-8                10000000               163 ns/op              72 B/op          3 allocs/op
BenchmarkStructSimpleCrossFieldFailure-8                         2000000               645 ns/op             304 B/op          8 allocs/op
BenchmarkStructSimpleCrossFieldFailureParallel-8                 5000000               285 ns/op             304 B/op          8 allocs/op
BenchmarkStructSimpleCrossStructCrossFieldSuccess-8              3000000               588 ns/op              80 B/op          4 allocs/op
BenchmarkStructSimpleCrossStructCrossFieldSuccessParallel-8     10000000               221 ns/op              80 B/op          4 allocs/op
BenchmarkStructSimpleCrossStructCrossFieldFailure-8              2000000               868 ns/op             320 B/op          9 allocs/op
BenchmarkStructSimpleCrossStructCrossFieldFailureParallel-8      5000000               337 ns/op             320 B/op          9 allocs/op
BenchmarkStructSimpleSuccess-8                                   5000000               260 ns/op               0 B/op          0 allocs/op
BenchmarkStructSimpleSuccessParallel-8                          20000000                90.6 ns/op             0 B/op          0 allocs/op
BenchmarkStructSimpleFailure-8                                   2000000               619 ns/op             424 B/op          9 allocs/op
BenchmarkStructSimpleFailureParallel-8                           5000000               296 ns/op             424 B/op          9 allocs/op
BenchmarkStructComplexSuccess-8                                  1000000              1454 ns/op             128 B/op          8 allocs/op
BenchmarkStructComplexSuccessParallel-8                          3000000               579 ns/op             128 B/op          8 allocs/op
BenchmarkStructComplexFailure-8                                   300000              4140 ns/op            3041 B/op         53 allocs/op
BenchmarkStructComplexFailureParallel-8                          1000000              2127 ns/op            3041 B/op         53 allocs/op
BenchmarkOneof-8                                                10000000               140 ns/op               0 B/op          0 allocs/op
BenchmarkOneofParallel-8                                        20000000                70.1 ns/op             0 B/op          0 allocs/op

View on Github

2 - Gody:

🎈 A lightweight struct validator for Go.

Installation

go get github.com/guiferpa/gody/v2

Usage

package main

import (
    "encoding/json"
    "fmt"
    "net/http"

    gody "github.com/guiferpa/gody/v2"
    "github.com/guiferpa/gody/v2/rule"
) 

type RequestBody struct {
    Name string `json:"name" validate:"not_empty"`
    Age  int    `json:"age" validate:"min=21"`
}

func HTTPHandler(v *gody.Validator) http.HandlerFunc {
    return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
        var body RequestBody
        if err := json.NewDecoder(r.Body).Decode(&body); err != nil {
	    ...
    	}
	defer r.Body.Close()

	if isValidated, err := v.Validate(body); err != nil {
	    ...                                                                                
	}
    })
}

func main() {
    validator := gody.NewValidator()

    validator.AddRules(rule.NotEmpty, rule.Min)

    port := ":3000"
    http.ListenAndServe(port, HTTPHandler(validator))
}

Others ways for validation

There are others ways to valid a struct, take a look on functions below:

  • RawValidate - It's a function that make validate with no rule, it's necessary put the struct for validation, some rule(s) and tag name.
gody.RawValidate(interface{}, string, []gody.Rule) (bool, error)
  • Validate - It's a function that make validate with no rule, it's necessary put the struct for validation and some rule(s).
gody.Validate(interface{}, []gody.Rule) (bool, error)
  • RawDefaultValidate - It's a function that already have built-in rules configured, it's necessary put the struct for validation, tag name and optional custom rule(s).
gody.RawDefaultValidate(interface{}, string, []gody.Rule) (bool, error)
  • DefaultValidate - It's a function that already have built-in rules configured, it's necessary put the struct for validation and optional custom rule(s).
gody.DefaultValidate(interface{}, []gody.Rule) (bool, error)

View on Github

3 - Govalid:

Fast, tag-based validation for structs.

Example

package main

import (
	"fmt"
	"log"
	"strings"

	"github.com/twharmon/govalid"
)

type Post struct {
	// ID has no constraints
	ID int

	// Title is required, must be at least 3 characters long, cannot be
	// more than 20 characters long, and must match ^[a-zA-Z ]+$
	Title string `govalid:"req|min:3|max:20|regex:^[a-zA-Z ]+$"`

	// Body is not required, cannot be more than 10000 charachers,
	// and must be "fun" (a custom rule defined below).
	Body string `govalid:"max:10000|fun"`

	// Category is not required, but if not zero value ("") it must be
	// either "announcement" or "bookreview".
	Category string `govalid:"in:announcement,bookreview"`
}

var v = govalid.New()

func main() {
	// Add Custom validation to the struct `Post`
	v.AddCustom(Post{}, func(val interface{}) string {
		post := val.(*Post)
		if post.Category != "" && !strings.Contains(post.Body, post.Category) {
			return fmt.Sprintf("Body must contain %s", post.Category)
		}
		return ""
	})
	
	// Add custom string "fun" that can be used on any string field
	// in any struct.
	v.AddCustomStringRule("fun", func(field string, value string) string {
		if float64(strings.Count(value, "!")) / float64(utf8.RuneCountInString(value)) > 0.001 {
			return ""
		}
		return fmt.Sprintf("%s must contain more exclamation marks", field)
	})

	p := Post{
		ID:       5,
		Title:    "Hi",
		Body:     "Hello world!",
		Category: "announcement",
	}

	vio, err := v.Violations(&p)
	if err != nil {
		log.Fatalln(err)
	}

	fmt.Println(vio)
}

Benchmarks

BenchmarkValidatorStringReqInvalid	        267.3 ns/op	      48 B/op	       3 allocs/op
BenchmarkValidatorStringReqValid	        92.35 ns/op	      16 B/op	       1 allocs/op
BenchmarkValidatorsVariety	                1484 ns/op	     297 B/op	      15 allocs/op

Contribute

Make a pull request.

View on Github

4 - Govalidator:

Validators and sanitizers for strings, numerics, slices and structs.

Installation

Make sure that Go is installed on your computer. Type the following command in your terminal:

go get github.com/asaskevich/govalidator

or you can get specified release of the package with gopkg.in:

go get gopkg.in/asaskevich/govalidator.v10

After it the package is ready to use.

Import package in your project

Add following line in your *.go file:

import "github.com/asaskevich/govalidator"

If you are unhappy to use long govalidator, you can do something like this:

import (
  valid "github.com/asaskevich/govalidator"
)

Activate behavior to require all fields have a validation tag by default

SetFieldsRequiredByDefault causes validation to fail when struct fields do not include validations or are not explicitly marked as exempt (using valid:"-" or valid:"email,optional"). A good place to activate this is a package init function or the main() function.

SetNilPtrAllowedByRequired causes validation to pass when struct fields marked by required are set to nil. This is disabled by default for consistency, but some packages that need to be able to determine between nil and zero value state can use this. If disabled, both nil and zero values cause validation errors.

import "github.com/asaskevich/govalidator"

func init() {
  govalidator.SetFieldsRequiredByDefault(true)
}

Here's some code to explain it:

// this struct definition will fail govalidator.ValidateStruct() (and the field values do not matter):
type exampleStruct struct {
  Name  string ``
  Email string `valid:"email"`
}

// this, however, will only fail when Email is empty or an invalid email address:
type exampleStruct2 struct {
  Name  string `valid:"-"`
  Email string `valid:"email"`
}

// lastly, this will only fail when Email is an invalid email address but not when it's empty:
type exampleStruct2 struct {
  Name  string `valid:"-"`
  Email string `valid:"email,optional"`
}

Recent breaking changes (see #123)

Custom validator function signature

A context was added as the second parameter, for structs this is the object being validated – this makes dependent validation possible.

import "github.com/asaskevich/govalidator"

// old signature
func(i interface{}) bool

// new signature
func(i interface{}, o interface{}) bool

Adding a custom validator

This was changed to prevent data races when accessing custom validators.

import "github.com/asaskevich/govalidator"

// before
govalidator.CustomTypeTagMap["customByteArrayValidator"] = func(i interface{}, o interface{}) bool {
  // ...
}

// after
govalidator.CustomTypeTagMap.Set("customByteArrayValidator", func(i interface{}, o interface{}) bool {
  // ...
})

View on Github

5 - Govalidator:

Validate Golang request data with simple rules. Highly inspired by Laravel's request validation.

Installation

Install the package using

$ go get github.com/thedevsaddam/govalidator
// or
$ go get gopkg.in/thedevsaddam/govalidator.v1

Usage

To use the package import it in your *.go code

import "github.com/thedevsaddam/govalidator"
// or
import "gopkg.in/thedevsaddam/govalidator.v1"

Example

Validate form-data, x-www-form-urlencoded and query params

package main

import (
	"encoding/json"
	"fmt"
	"net/http"

	"github.com/thedevsaddam/govalidator"
)

func handler(w http.ResponseWriter, r *http.Request) {
	rules := govalidator.MapData{
		"username": []string{"required", "between:3,8"},
		"email":    []string{"required", "min:4", "max:20", "email"},
		"web":      []string{"url"},
		"phone":    []string{"digits:11"},
		"agree":    []string{"bool"},
		"dob":      []string{"date"},
	}

	messages := govalidator.MapData{
		"username": []string{"required:আপনাকে অবশ্যই ইউজারনেম দিতে হবে", "between:ইউজারনেম অবশ্যই ৩-৮ অক্ষর হতে হবে"},
		"phone":    []string{"digits:ফোন নাম্বার অবশ্যই ১১ নম্বারের হতে হবে"},
	}

	opts := govalidator.Options{
		Request:         r,        // request object
		Rules:           rules,    // rules map
		Messages:        messages, // custom message map (Optional)
		RequiredDefault: true,     // all the field to be pass the rules
	}
	v := govalidator.New(opts)
	e := v.Validate()
	err := map[string]interface{}{"validationError": e}
	w.Header().Set("Content-type", "application/json")
	json.NewEncoder(w).Encode(err)
}

func main() {
	http.HandleFunc("/", handler)
	fmt.Println("Listening on port: 9000")
	http.ListenAndServe(":9000", nil)
}

Send request to the server using curl or postman: curl GET "http://localhost:9000?web=&phone=&zip=&dob=&agree="

View on Github

6 - Jio:

Jio is a json schema validator similar to joi.

Why use jio?

Parameter validation in Golang is really a cursing problem. Defining tags on structs is not easy to extend rules, handwritten validation code makes logic code cumbersome, and the initial zero value of the struct field will also interfere with the validation.

jio tries validate json raw data before deserialization to avoid these problems. Defining validation rules as Schema is easy to read and easy to extend (Inspired by Hapi.js joi library). Rules within Schema can be validated in the order of registration, and context can be used to exchange data between rules, and can access other field data even within a single rule, etc.

jio provides a flexible enough way to make your validation simple and efficient!

How to use?

Validate json string

package main

import (
    "log"

    "github.com/faceair/jio"
)

func main() {
    data := []byte(`{
        "debug": "on",
        "window": {
            "title": "Sample Widget",
            "size": [500, 500]
        }
    }`)
    _, err := jio.ValidateJSON(&data, jio.Object().Keys(jio.K{
        "debug": jio.Bool().Truthy("on").Required(),
        "window": jio.Object().Keys(jio.K{
            "title": jio.String().Min(3).Max(18),
            "size":  jio.Array().Items(jio.Number().Integer()).Length(2).Required(),
        }).Without("name", "title").Required(),
    }))
    if err != nil {
        panic(err)
    }
    log.Printf("%s", data) // {"debug":true,"window":{"size":[500,500],"title":"Sample Widget"}}
}

The above schema defines the following constraints:

  • debug
    • not empty, must be a boolean value when validation end
    • allow on string instead of true
  • window
    • not empty, object
    • not allowed for both name and title
    • The following elements exist
      • title
        • string, can be empty
        • length is between 3 and 18 when not empty
      • size
        • array, not empty
        • there are two child elements of the integer type

Using middleware to validate request body

Take chi as an example, the other frameworks are similar.

package main

import (
    "io/ioutil"
    "net/http"

    "github.com/faceair/jio"
    "github.com/go-chi/chi"
)

func main() {
    r := chi.NewRouter()
    r.Route("/people", func(r chi.Router) {
        r.With(jio.ValidateBody(jio.Object().Keys(jio.K{
            "name":  jio.String().Min(3).Max(10).Required(),
            "age":   jio.Number().Integer().Min(0).Max(100).Required(),
            "phone": jio.String().Regex(`^1[34578]\d{9}$`).Required(),
        }), jio.DefaultErrorHandler)).Post("/", func(w http.ResponseWriter, r *http.Request) {
            body, err := ioutil.ReadAll(r.Body)
            if err != nil {
                panic(err)
            }
            w.Header().Set("Content-Type", "application/json; charset=utf-8")
            w.WriteHeader(http.StatusOK)
            w.Write(body)
        })
    })
    http.ListenAndServe(":8080", r)
}

The second parameter of jio.ValidateBody is called for error handling when the validation fails.

View on Github

7 - Ozzo-validation:

Supports validation of various data types (structs, strings, maps, slices, etc.) with configurable and extensible validation rules specified in usual code constructs instead of struct tags.

Description

ozzo-validation is a Go package that provides configurable and extensible data validation capabilities. It has the following features:

  • use normal programming constructs rather than error-prone struct tags to specify how data should be validated.
  • can validate data of different types, e.g., structs, strings, byte slices, slices, maps, arrays.
  • can validate custom data types as long as they implement the Validatable interface.
  • can validate data types that implement the sql.Valuer interface (e.g. sql.NullString).
  • customizable and well-formatted validation errors.
  • error code and message translation support.
  • provide a rich set of validation rules right out of box.
  • extremely easy to create and use custom validation rules.

For an example on how this library is used in an application, please refer to go-rest-api which is a starter kit for building RESTful APIs in Go.

Requirements

Go 1.13 or above.

Getting Started

The ozzo-validation package mainly includes a set of validation rules and two validation methods. You use validation rules to describe how a value should be considered valid, and you call either validation.Validate() or validation.ValidateStruct() to validate the value.

Installation

Run the following command to install the package:

go get github.com/go-ozzo/ozzo-validation

Validating a Simple Value

For a simple value, such as a string or an integer, you may use validation.Validate() to validate it. For example,

package main

import (
	"fmt"

	"github.com/go-ozzo/ozzo-validation/v4"
	"github.com/go-ozzo/ozzo-validation/v4/is"
)

func main() {
	data := "example"
	err := validation.Validate(data,
		validation.Required,       // not empty
		validation.Length(5, 100), // length between 5 and 100
		is.URL,                    // is a valid URL
	)
	fmt.Println(err)
	// Output:
	// must be a valid URL
}

The method validation.Validate() will run through the rules in the order that they are listed. If a rule fails the validation, the method will return the corresponding error and skip the rest of the rules. The method will return nil if the value passes all validation rules.

Validating a Struct

For a struct value, you usually want to check if its fields are valid. For example, in a RESTful application, you may unmarshal the request payload into a struct and then validate the struct fields. If one or multiple fields are invalid, you may want to get an error describing which fields are invalid. You can use validation.ValidateStruct() to achieve this purpose. A single struct can have rules for multiple fields, and a field can be associated with multiple rules. For example,

type Address struct {
	Street string
	City   string
	State  string
	Zip    string
}

func (a Address) Validate() error {
	return validation.ValidateStruct(&a,
		// Street cannot be empty, and the length must between 5 and 50
		validation.Field(&a.Street, validation.Required, validation.Length(5, 50)),
		// City cannot be empty, and the length must between 5 and 50
		validation.Field(&a.City, validation.Required, validation.Length(5, 50)),
		// State cannot be empty, and must be a string consisting of two letters in upper case
		validation.Field(&a.State, validation.Required, validation.Match(regexp.MustCompile("^[A-Z]{2}$"))),
		// State cannot be empty, and must be a string consisting of five digits
		validation.Field(&a.Zip, validation.Required, validation.Match(regexp.MustCompile("^[0-9]{5}$"))),
	)
}

a := Address{
    Street: "123",
    City:   "Unknown",
    State:  "Virginia",
    Zip:    "12345",
}

err := a.Validate()
fmt.Println(err)
// Output:
// Street: the length must be between 5 and 50; State: must be in a valid format.

Note that when calling validation.ValidateStruct to validate a struct, you should pass to the method a pointer to the struct instead of the struct itself. Similarly, when calling validation.Field to specify the rules for a struct field, you should use a pointer to the struct field.

When the struct validation is performed, the fields are validated in the order they are specified in ValidateStruct. And when each field is validated, its rules are also evaluated in the order they are associated with the field. If a rule fails, an error is recorded for that field, and the validation will continue with the next field.

View on Github

8 - Terraform-validator:

A norms and conventions validator for Terraform.

This tool will help you ensure that a terraform folder answer to your norms and conventions rules. This can be really useful in several cases :

  • You're a team that want to have a clean and maintainable code.
  • You're a lonely developer that develop a lot of modules and you want to have a certain consistency between them.

Features:

  •  make sure that the block names match a certain pattern.
  •  make sure that the code is properly dispatched. To do this you can decide what type of block can contain each file (for example output blocks must be in outputs.tf).
  •  ensure that mandatory .tf files are present.
  •  ensure that the terraform version has been defined.
  •  ensure that the providers' version has been defined.
  •  make sure that the variables and/or outputs blocks have the description argument filled in.
  •  layered terraform folders (test recursively).

⚠️ Terraform 0.12+ is supported only by the versions 2.0.0 and higher.

Documentation

Please find the full documentation here (ReadTheDocs).

View on Github

9 - Validate:

Go package for data validation and filtering. support validate Map, Struct, Request(Form, JSON, url.Values, Uploaded Files) data and more features.

validate is a generic Go data validate and filter tool library.

  • Support quick validate Map, Struct, Request(Form, JSON, url.Values, UploadedFile) data
    • Validating http.Request automatically collects data based on the request Content-Type value
    • Supports checking each child value in a slice. eg: v.StringRule("tags.*", "required|string")
  • Support filter/sanitize/convert data before validate
  • Support add custom filter/validator func
  • Support scene settings, verify different fields in different scenes
  • Support custom error messages, field translates.
    • Can use message, label tags in struct
  • Customizable i18n aware error messages, built in en, zh-CN, zh-TW
  • Built-in common data type filter/converter. see Built In Filters
  • Many commonly used validators have been built in(> 70), see Built In Validators
  • Can use validate in any frameworks, such as Gin, Echo, Chi and more
  • Supports direct use of rules to validate value. eg: validate.Val("xyz@mail.com", "required|email")

Inspired the projects albrow/forms and asaskevich/govalidator and inhere/php-validate. Thank you very much

Validate Struct

Use the validate tag of the structure, you can quickly config a structure.

Config the struct use tags

Field translations and error messages for structs can be quickly configured using the message and label tags.

  • Support configuration field mapping through structure tag, read the value of json tag by default
  • Support configuration error message via structure's message tag
  • Support configuration field translation via structure's label tag
package main

import (
	"fmt"
	"time"

	"github.com/gookit/validate"
)

// UserForm struct
type UserForm struct {
	Name     string    `validate:"required|min_len:7" message:"required:{field} is required" label:"User Name"`
	Email    string    `validate:"email" message:"email is invalid" label:"User Email"`
	Age      int       `validate:"required|int|min:1|max:99" message:"int:age must int|min:age min value is 1"`
	CreateAt int       `validate:"min:1"`
	Safe     int       `validate:"-"`
	UpdateAt time.Time `validate:"required" message:"update time is required"`
	Code     string    `validate:"customValidator"`
	// ExtInfo nested struct
	ExtInfo struct{
		Homepage string `validate:"required" label:"Home Page"`
		CityName string
	} `validate:"required" label:"Home Page"`
}

// CustomValidator custom validator in the source struct.
func (f UserForm) CustomValidator(val string) bool {
	return len(val) == 4
}

View on Github

10 - Validate:

This package provides a framework for writing validations for Go applications. It does provide you with few validators, but if you need others you can easly build them.

Installation

$ go get github.com/gobuffalo/validate

Usage

Using validate is pretty easy, just define some Validator objects and away you go.

Here is a pretty simple example:

package main

import (
	"log"

	v "github.com/gobuffalo/validate"
)

type User struct {
	Name  string
	Email string
}

func (u *User) IsValid(errors *v.Errors) {
	if u.Name == "" {
		errors.Add("name", "Name must not be blank!")
	}
	if u.Email == "" {
		errors.Add("email", "Email must not be blank!")
	}
}

func main() {
	u := User{Name: "", Email: ""}
	errors := v.Validate(&u)
	log.Println(errors.Errors)
  // map[name:[Name must not be blank!] email:[Email must not be blank!]]
}

In the previous example I wrote a single Validator for the User struct. To really get the benefit of using go-validator, as well as the Go language, I would recommend creating distinct validators for each thing you want to validate, that way they can be run concurrently.

package main

import (
	"fmt"
	"log"
	"strings"

	v "github.com/gobuffalo/validate"
)

type User struct {
	Name  string
	Email string
}

type PresenceValidator struct {
	Field string
	Value string
}

func (v *PresenceValidator) IsValid(errors *v.Errors) {
	if v.Value == "" {
		errors.Add(strings.ToLower(v.Field), fmt.Sprintf("%s must not be blank!", v.Field))
	}
}

func main() {
	u := User{Name: "", Email: ""}
	errors := v.Validate(&PresenceValidator{"Email", u.Email}, &PresenceValidator{"Name", u.Name})
	log.Println(errors.Errors)
        // map[name:[Name must not be blank!] email:[Email must not be blank!]]
}

That's really it. Pretty simple and straight-forward Just a nice clean framework for writing your own validators. Use in good health.

View on Github

Thank you for following this article.

Related videos:

Golang Microservices: Validations

#go #golang #validators 

What is GEEK

Buddha Community

10 Popular Golang Libraries for Validation

10 Popular Golang Libraries for Validation

In today's post we will learn about 10 Popular Golang Libraries for Validation. 

What is Validation?

Validation is a simple concept to understand but difficult to put into practice.

Validation is the recognition and acceptance of another persons internal experience as being valid. Emotional validation is distinguished from emotional invalidation, in which your own or another persons emotional experiences are rejected, ignored, or judged. Self-validation is the recognition and acknowledgement of your own internal experience.

Validation does not mean agreeing with or supporting feelings or thoughts. Validating does not mean love. You can validate someone you don’t like even though you probably wouldn’t want to.

Table of contents:

  • Validator - Go Struct and Field validation, including Cross Field, Cross Struct, Map, Slice and Array diving.
  • Gody - 🎈 A lightweight struct validator for Go.
  • Govalid - Fast, tag-based validation for structs.
  • Govalidator - Validators and sanitizers for strings, numerics, slices and structs.
  • Govalidator - Validate Golang request data with simple rules. Highly inspired by Laravel's request validation.
  • Jio - Jio is a json schema validator similar to joi.
  • Ozzo-validation - Supports validation of various data types (structs, strings, maps, slices, etc.) with configurable and extensible validation rules specified in usual code constructs instead of struct tags.
  • Terraform-validator - A norms and conventions validator for Terraform.
  • Validate - Go package for data validation and filtering. support validate Map, Struct, Request(Form, JSON, url.Values, Uploaded Files) data and more features.
  • Validate - This package provides a framework for writing validations for Go applications.

1 - Validator:

Go Struct and Field validation, including Cross Field, Cross Struct, Map, Slice and Array diving.

Package validator implements value validations for structs and individual fields based on tags.

It has the following unique features:

  • Cross Field and Cross Struct validations by using validation tags or custom validators.
  • Slice, Array and Map diving, which allows any or all levels of a multidimensional field to be validated.
  • Ability to dive into both map keys and values for validation
  • Handles type interface by determining it's underlying type prior to validation.
  • Handles custom field types such as sql driver Valuer see Valuer
  • Alias validation tags, which allows for mapping of several validations to a single tag for easier defining of validations on structs
  • Extraction of custom defined Field Name e.g. can specify to extract the JSON name while validating and have it available in the resulting FieldError
  • Customizable i18n aware error messages.
  • Default validator for the gin web framework; upgrading from v8 to v9 in gin see here

Installation

Use go get.

go get github.com/go-playground/validator/v10

Then import the validator package into your own code.

import "github.com/go-playground/validator/v10"

Error Return Value

Validation functions return type error

They return type error to avoid the issue discussed in the following, where err is always != nil:

Validator returns only InvalidValidationError for bad validation input, nil or ValidationErrors as type error; so, in your code all you need to do is check if the error returned is not nil, and if it's not check if error is InvalidValidationError ( if necessary, most of the time it isn't ) type cast it to type ValidationErrors like so:

err := validate.Struct(mystruct)
validationErrors := err.(validator.ValidationErrors)

Usage and documentation

Please see https://pkg.go.dev/github.com/go-playground/validator/v10 for detailed usage docs.

Benchmarks

Run on MacBook Pro (15-inch, 2017) go version go1.10.2 darwin/amd64

goos: darwin
goarch: amd64
pkg: github.com/go-playground/validator
BenchmarkFieldSuccess-8                                         20000000                83.6 ns/op             0 B/op          0 allocs/op
BenchmarkFieldSuccessParallel-8                                 50000000                26.8 ns/op             0 B/op          0 allocs/op
BenchmarkFieldFailure-8                                          5000000               291 ns/op             208 B/op          4 allocs/op
BenchmarkFieldFailureParallel-8                                 20000000               107 ns/op             208 B/op          4 allocs/op
BenchmarkFieldArrayDiveSuccess-8                                 2000000               623 ns/op             201 B/op         11 allocs/op
BenchmarkFieldArrayDiveSuccessParallel-8                        10000000               237 ns/op             201 B/op         11 allocs/op
BenchmarkFieldArrayDiveFailure-8                                 2000000               859 ns/op             412 B/op         16 allocs/op
BenchmarkFieldArrayDiveFailureParallel-8                         5000000               335 ns/op             413 B/op         16 allocs/op
BenchmarkFieldMapDiveSuccess-8                                   1000000              1292 ns/op             432 B/op         18 allocs/op
BenchmarkFieldMapDiveSuccessParallel-8                           3000000               467 ns/op             432 B/op         18 allocs/op
BenchmarkFieldMapDiveFailure-8                                   1000000              1082 ns/op             512 B/op         16 allocs/op
BenchmarkFieldMapDiveFailureParallel-8                           5000000               425 ns/op             512 B/op         16 allocs/op
BenchmarkFieldMapDiveWithKeysSuccess-8                           1000000              1539 ns/op             480 B/op         21 allocs/op
BenchmarkFieldMapDiveWithKeysSuccessParallel-8                   3000000               613 ns/op             480 B/op         21 allocs/op
BenchmarkFieldMapDiveWithKeysFailure-8                           1000000              1413 ns/op             721 B/op         21 allocs/op
BenchmarkFieldMapDiveWithKeysFailureParallel-8                   3000000               575 ns/op             721 B/op         21 allocs/op
BenchmarkFieldCustomTypeSuccess-8                               10000000               216 ns/op              32 B/op          2 allocs/op
BenchmarkFieldCustomTypeSuccessParallel-8                       20000000                82.2 ns/op            32 B/op          2 allocs/op
BenchmarkFieldCustomTypeFailure-8                                5000000               274 ns/op             208 B/op          4 allocs/op
BenchmarkFieldCustomTypeFailureParallel-8                       20000000               116 ns/op             208 B/op          4 allocs/op
BenchmarkFieldOrTagSuccess-8                                     2000000               740 ns/op              16 B/op          1 allocs/op
BenchmarkFieldOrTagSuccessParallel-8                             3000000               474 ns/op              16 B/op          1 allocs/op
BenchmarkFieldOrTagFailure-8                                     3000000               471 ns/op             224 B/op          5 allocs/op
BenchmarkFieldOrTagFailureParallel-8                             3000000               414 ns/op             224 B/op          5 allocs/op
BenchmarkStructLevelValidationSuccess-8                         10000000               213 ns/op              32 B/op          2 allocs/op
BenchmarkStructLevelValidationSuccessParallel-8                 20000000                91.8 ns/op            32 B/op          2 allocs/op
BenchmarkStructLevelValidationFailure-8                          3000000               473 ns/op             304 B/op          8 allocs/op
BenchmarkStructLevelValidationFailureParallel-8                 10000000               234 ns/op             304 B/op          8 allocs/op
BenchmarkStructSimpleCustomTypeSuccess-8                         5000000               385 ns/op              32 B/op          2 allocs/op
BenchmarkStructSimpleCustomTypeSuccessParallel-8                10000000               161 ns/op              32 B/op          2 allocs/op
BenchmarkStructSimpleCustomTypeFailure-8                         2000000               640 ns/op             424 B/op          9 allocs/op
BenchmarkStructSimpleCustomTypeFailureParallel-8                 5000000               318 ns/op             440 B/op         10 allocs/op
BenchmarkStructFilteredSuccess-8                                 2000000               597 ns/op             288 B/op          9 allocs/op
BenchmarkStructFilteredSuccessParallel-8                        10000000               266 ns/op             288 B/op          9 allocs/op
BenchmarkStructFilteredFailure-8                                 3000000               454 ns/op             256 B/op          7 allocs/op
BenchmarkStructFilteredFailureParallel-8                        10000000               214 ns/op             256 B/op          7 allocs/op
BenchmarkStructPartialSuccess-8                                  3000000               502 ns/op             256 B/op          6 allocs/op
BenchmarkStructPartialSuccessParallel-8                         10000000               225 ns/op             256 B/op          6 allocs/op
BenchmarkStructPartialFailure-8                                  2000000               702 ns/op             480 B/op         11 allocs/op
BenchmarkStructPartialFailureParallel-8                          5000000               329 ns/op             480 B/op         11 allocs/op
BenchmarkStructExceptSuccess-8                                   2000000               793 ns/op             496 B/op         12 allocs/op
BenchmarkStructExceptSuccessParallel-8                          10000000               193 ns/op             240 B/op          5 allocs/op
BenchmarkStructExceptFailure-8                                   2000000               639 ns/op             464 B/op         10 allocs/op
BenchmarkStructExceptFailureParallel-8                           5000000               300 ns/op             464 B/op         10 allocs/op
BenchmarkStructSimpleCrossFieldSuccess-8                         3000000               417 ns/op              72 B/op          3 allocs/op
BenchmarkStructSimpleCrossFieldSuccessParallel-8                10000000               163 ns/op              72 B/op          3 allocs/op
BenchmarkStructSimpleCrossFieldFailure-8                         2000000               645 ns/op             304 B/op          8 allocs/op
BenchmarkStructSimpleCrossFieldFailureParallel-8                 5000000               285 ns/op             304 B/op          8 allocs/op
BenchmarkStructSimpleCrossStructCrossFieldSuccess-8              3000000               588 ns/op              80 B/op          4 allocs/op
BenchmarkStructSimpleCrossStructCrossFieldSuccessParallel-8     10000000               221 ns/op              80 B/op          4 allocs/op
BenchmarkStructSimpleCrossStructCrossFieldFailure-8              2000000               868 ns/op             320 B/op          9 allocs/op
BenchmarkStructSimpleCrossStructCrossFieldFailureParallel-8      5000000               337 ns/op             320 B/op          9 allocs/op
BenchmarkStructSimpleSuccess-8                                   5000000               260 ns/op               0 B/op          0 allocs/op
BenchmarkStructSimpleSuccessParallel-8                          20000000                90.6 ns/op             0 B/op          0 allocs/op
BenchmarkStructSimpleFailure-8                                   2000000               619 ns/op             424 B/op          9 allocs/op
BenchmarkStructSimpleFailureParallel-8                           5000000               296 ns/op             424 B/op          9 allocs/op
BenchmarkStructComplexSuccess-8                                  1000000              1454 ns/op             128 B/op          8 allocs/op
BenchmarkStructComplexSuccessParallel-8                          3000000               579 ns/op             128 B/op          8 allocs/op
BenchmarkStructComplexFailure-8                                   300000              4140 ns/op            3041 B/op         53 allocs/op
BenchmarkStructComplexFailureParallel-8                          1000000              2127 ns/op            3041 B/op         53 allocs/op
BenchmarkOneof-8                                                10000000               140 ns/op               0 B/op          0 allocs/op
BenchmarkOneofParallel-8                                        20000000                70.1 ns/op             0 B/op          0 allocs/op

View on Github

2 - Gody:

🎈 A lightweight struct validator for Go.

Installation

go get github.com/guiferpa/gody/v2

Usage

package main

import (
    "encoding/json"
    "fmt"
    "net/http"

    gody "github.com/guiferpa/gody/v2"
    "github.com/guiferpa/gody/v2/rule"
) 

type RequestBody struct {
    Name string `json:"name" validate:"not_empty"`
    Age  int    `json:"age" validate:"min=21"`
}

func HTTPHandler(v *gody.Validator) http.HandlerFunc {
    return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
        var body RequestBody
        if err := json.NewDecoder(r.Body).Decode(&body); err != nil {
	    ...
    	}
	defer r.Body.Close()

	if isValidated, err := v.Validate(body); err != nil {
	    ...                                                                                
	}
    })
}

func main() {
    validator := gody.NewValidator()

    validator.AddRules(rule.NotEmpty, rule.Min)

    port := ":3000"
    http.ListenAndServe(port, HTTPHandler(validator))
}

Others ways for validation

There are others ways to valid a struct, take a look on functions below:

  • RawValidate - It's a function that make validate with no rule, it's necessary put the struct for validation, some rule(s) and tag name.
gody.RawValidate(interface{}, string, []gody.Rule) (bool, error)
  • Validate - It's a function that make validate with no rule, it's necessary put the struct for validation and some rule(s).
gody.Validate(interface{}, []gody.Rule) (bool, error)
  • RawDefaultValidate - It's a function that already have built-in rules configured, it's necessary put the struct for validation, tag name and optional custom rule(s).
gody.RawDefaultValidate(interface{}, string, []gody.Rule) (bool, error)
  • DefaultValidate - It's a function that already have built-in rules configured, it's necessary put the struct for validation and optional custom rule(s).
gody.DefaultValidate(interface{}, []gody.Rule) (bool, error)

View on Github

3 - Govalid:

Fast, tag-based validation for structs.

Example

package main

import (
	"fmt"
	"log"
	"strings"

	"github.com/twharmon/govalid"
)

type Post struct {
	// ID has no constraints
	ID int

	// Title is required, must be at least 3 characters long, cannot be
	// more than 20 characters long, and must match ^[a-zA-Z ]+$
	Title string `govalid:"req|min:3|max:20|regex:^[a-zA-Z ]+$"`

	// Body is not required, cannot be more than 10000 charachers,
	// and must be "fun" (a custom rule defined below).
	Body string `govalid:"max:10000|fun"`

	// Category is not required, but if not zero value ("") it must be
	// either "announcement" or "bookreview".
	Category string `govalid:"in:announcement,bookreview"`
}

var v = govalid.New()

func main() {
	// Add Custom validation to the struct `Post`
	v.AddCustom(Post{}, func(val interface{}) string {
		post := val.(*Post)
		if post.Category != "" && !strings.Contains(post.Body, post.Category) {
			return fmt.Sprintf("Body must contain %s", post.Category)
		}
		return ""
	})
	
	// Add custom string "fun" that can be used on any string field
	// in any struct.
	v.AddCustomStringRule("fun", func(field string, value string) string {
		if float64(strings.Count(value, "!")) / float64(utf8.RuneCountInString(value)) > 0.001 {
			return ""
		}
		return fmt.Sprintf("%s must contain more exclamation marks", field)
	})

	p := Post{
		ID:       5,
		Title:    "Hi",
		Body:     "Hello world!",
		Category: "announcement",
	}

	vio, err := v.Violations(&p)
	if err != nil {
		log.Fatalln(err)
	}

	fmt.Println(vio)
}

Benchmarks

BenchmarkValidatorStringReqInvalid	        267.3 ns/op	      48 B/op	       3 allocs/op
BenchmarkValidatorStringReqValid	        92.35 ns/op	      16 B/op	       1 allocs/op
BenchmarkValidatorsVariety	                1484 ns/op	     297 B/op	      15 allocs/op

Contribute

Make a pull request.

View on Github

4 - Govalidator:

Validators and sanitizers for strings, numerics, slices and structs.

Installation

Make sure that Go is installed on your computer. Type the following command in your terminal:

go get github.com/asaskevich/govalidator

or you can get specified release of the package with gopkg.in:

go get gopkg.in/asaskevich/govalidator.v10

After it the package is ready to use.

Import package in your project

Add following line in your *.go file:

import "github.com/asaskevich/govalidator"

If you are unhappy to use long govalidator, you can do something like this:

import (
  valid "github.com/asaskevich/govalidator"
)

Activate behavior to require all fields have a validation tag by default

SetFieldsRequiredByDefault causes validation to fail when struct fields do not include validations or are not explicitly marked as exempt (using valid:"-" or valid:"email,optional"). A good place to activate this is a package init function or the main() function.

SetNilPtrAllowedByRequired causes validation to pass when struct fields marked by required are set to nil. This is disabled by default for consistency, but some packages that need to be able to determine between nil and zero value state can use this. If disabled, both nil and zero values cause validation errors.

import "github.com/asaskevich/govalidator"

func init() {
  govalidator.SetFieldsRequiredByDefault(true)
}

Here's some code to explain it:

// this struct definition will fail govalidator.ValidateStruct() (and the field values do not matter):
type exampleStruct struct {
  Name  string ``
  Email string `valid:"email"`
}

// this, however, will only fail when Email is empty or an invalid email address:
type exampleStruct2 struct {
  Name  string `valid:"-"`
  Email string `valid:"email"`
}

// lastly, this will only fail when Email is an invalid email address but not when it's empty:
type exampleStruct2 struct {
  Name  string `valid:"-"`
  Email string `valid:"email,optional"`
}

Recent breaking changes (see #123)

Custom validator function signature

A context was added as the second parameter, for structs this is the object being validated – this makes dependent validation possible.

import "github.com/asaskevich/govalidator"

// old signature
func(i interface{}) bool

// new signature
func(i interface{}, o interface{}) bool

Adding a custom validator

This was changed to prevent data races when accessing custom validators.

import "github.com/asaskevich/govalidator"

// before
govalidator.CustomTypeTagMap["customByteArrayValidator"] = func(i interface{}, o interface{}) bool {
  // ...
}

// after
govalidator.CustomTypeTagMap.Set("customByteArrayValidator", func(i interface{}, o interface{}) bool {
  // ...
})

View on Github

5 - Govalidator:

Validate Golang request data with simple rules. Highly inspired by Laravel's request validation.

Installation

Install the package using

$ go get github.com/thedevsaddam/govalidator
// or
$ go get gopkg.in/thedevsaddam/govalidator.v1

Usage

To use the package import it in your *.go code

import "github.com/thedevsaddam/govalidator"
// or
import "gopkg.in/thedevsaddam/govalidator.v1"

Example

Validate form-data, x-www-form-urlencoded and query params

package main

import (
	"encoding/json"
	"fmt"
	"net/http"

	"github.com/thedevsaddam/govalidator"
)

func handler(w http.ResponseWriter, r *http.Request) {
	rules := govalidator.MapData{
		"username": []string{"required", "between:3,8"},
		"email":    []string{"required", "min:4", "max:20", "email"},
		"web":      []string{"url"},
		"phone":    []string{"digits:11"},
		"agree":    []string{"bool"},
		"dob":      []string{"date"},
	}

	messages := govalidator.MapData{
		"username": []string{"required:আপনাকে অবশ্যই ইউজারনেম দিতে হবে", "between:ইউজারনেম অবশ্যই ৩-৮ অক্ষর হতে হবে"},
		"phone":    []string{"digits:ফোন নাম্বার অবশ্যই ১১ নম্বারের হতে হবে"},
	}

	opts := govalidator.Options{
		Request:         r,        // request object
		Rules:           rules,    // rules map
		Messages:        messages, // custom message map (Optional)
		RequiredDefault: true,     // all the field to be pass the rules
	}
	v := govalidator.New(opts)
	e := v.Validate()
	err := map[string]interface{}{"validationError": e}
	w.Header().Set("Content-type", "application/json")
	json.NewEncoder(w).Encode(err)
}

func main() {
	http.HandleFunc("/", handler)
	fmt.Println("Listening on port: 9000")
	http.ListenAndServe(":9000", nil)
}

Send request to the server using curl or postman: curl GET "http://localhost:9000?web=&phone=&zip=&dob=&agree="

View on Github

6 - Jio:

Jio is a json schema validator similar to joi.

Why use jio?

Parameter validation in Golang is really a cursing problem. Defining tags on structs is not easy to extend rules, handwritten validation code makes logic code cumbersome, and the initial zero value of the struct field will also interfere with the validation.

jio tries validate json raw data before deserialization to avoid these problems. Defining validation rules as Schema is easy to read and easy to extend (Inspired by Hapi.js joi library). Rules within Schema can be validated in the order of registration, and context can be used to exchange data between rules, and can access other field data even within a single rule, etc.

jio provides a flexible enough way to make your validation simple and efficient!

How to use?

Validate json string

package main

import (
    "log"

    "github.com/faceair/jio"
)

func main() {
    data := []byte(`{
        "debug": "on",
        "window": {
            "title": "Sample Widget",
            "size": [500, 500]
        }
    }`)
    _, err := jio.ValidateJSON(&data, jio.Object().Keys(jio.K{
        "debug": jio.Bool().Truthy("on").Required(),
        "window": jio.Object().Keys(jio.K{
            "title": jio.String().Min(3).Max(18),
            "size":  jio.Array().Items(jio.Number().Integer()).Length(2).Required(),
        }).Without("name", "title").Required(),
    }))
    if err != nil {
        panic(err)
    }
    log.Printf("%s", data) // {"debug":true,"window":{"size":[500,500],"title":"Sample Widget"}}
}

The above schema defines the following constraints:

  • debug
    • not empty, must be a boolean value when validation end
    • allow on string instead of true
  • window
    • not empty, object
    • not allowed for both name and title
    • The following elements exist
      • title
        • string, can be empty
        • length is between 3 and 18 when not empty
      • size
        • array, not empty
        • there are two child elements of the integer type

Using middleware to validate request body

Take chi as an example, the other frameworks are similar.

package main

import (
    "io/ioutil"
    "net/http"

    "github.com/faceair/jio"
    "github.com/go-chi/chi"
)

func main() {
    r := chi.NewRouter()
    r.Route("/people", func(r chi.Router) {
        r.With(jio.ValidateBody(jio.Object().Keys(jio.K{
            "name":  jio.String().Min(3).Max(10).Required(),
            "age":   jio.Number().Integer().Min(0).Max(100).Required(),
            "phone": jio.String().Regex(`^1[34578]\d{9}$`).Required(),
        }), jio.DefaultErrorHandler)).Post("/", func(w http.ResponseWriter, r *http.Request) {
            body, err := ioutil.ReadAll(r.Body)
            if err != nil {
                panic(err)
            }
            w.Header().Set("Content-Type", "application/json; charset=utf-8")
            w.WriteHeader(http.StatusOK)
            w.Write(body)
        })
    })
    http.ListenAndServe(":8080", r)
}

The second parameter of jio.ValidateBody is called for error handling when the validation fails.

View on Github

7 - Ozzo-validation:

Supports validation of various data types (structs, strings, maps, slices, etc.) with configurable and extensible validation rules specified in usual code constructs instead of struct tags.

Description

ozzo-validation is a Go package that provides configurable and extensible data validation capabilities. It has the following features:

  • use normal programming constructs rather than error-prone struct tags to specify how data should be validated.
  • can validate data of different types, e.g., structs, strings, byte slices, slices, maps, arrays.
  • can validate custom data types as long as they implement the Validatable interface.
  • can validate data types that implement the sql.Valuer interface (e.g. sql.NullString).
  • customizable and well-formatted validation errors.
  • error code and message translation support.
  • provide a rich set of validation rules right out of box.
  • extremely easy to create and use custom validation rules.

For an example on how this library is used in an application, please refer to go-rest-api which is a starter kit for building RESTful APIs in Go.

Requirements

Go 1.13 or above.

Getting Started

The ozzo-validation package mainly includes a set of validation rules and two validation methods. You use validation rules to describe how a value should be considered valid, and you call either validation.Validate() or validation.ValidateStruct() to validate the value.

Installation

Run the following command to install the package:

go get github.com/go-ozzo/ozzo-validation

Validating a Simple Value

For a simple value, such as a string or an integer, you may use validation.Validate() to validate it. For example,

package main

import (
	"fmt"

	"github.com/go-ozzo/ozzo-validation/v4"
	"github.com/go-ozzo/ozzo-validation/v4/is"
)

func main() {
	data := "example"
	err := validation.Validate(data,
		validation.Required,       // not empty
		validation.Length(5, 100), // length between 5 and 100
		is.URL,                    // is a valid URL
	)
	fmt.Println(err)
	// Output:
	// must be a valid URL
}

The method validation.Validate() will run through the rules in the order that they are listed. If a rule fails the validation, the method will return the corresponding error and skip the rest of the rules. The method will return nil if the value passes all validation rules.

Validating a Struct

For a struct value, you usually want to check if its fields are valid. For example, in a RESTful application, you may unmarshal the request payload into a struct and then validate the struct fields. If one or multiple fields are invalid, you may want to get an error describing which fields are invalid. You can use validation.ValidateStruct() to achieve this purpose. A single struct can have rules for multiple fields, and a field can be associated with multiple rules. For example,

type Address struct {
	Street string
	City   string
	State  string
	Zip    string
}

func (a Address) Validate() error {
	return validation.ValidateStruct(&a,
		// Street cannot be empty, and the length must between 5 and 50
		validation.Field(&a.Street, validation.Required, validation.Length(5, 50)),
		// City cannot be empty, and the length must between 5 and 50
		validation.Field(&a.City, validation.Required, validation.Length(5, 50)),
		// State cannot be empty, and must be a string consisting of two letters in upper case
		validation.Field(&a.State, validation.Required, validation.Match(regexp.MustCompile("^[A-Z]{2}$"))),
		// State cannot be empty, and must be a string consisting of five digits
		validation.Field(&a.Zip, validation.Required, validation.Match(regexp.MustCompile("^[0-9]{5}$"))),
	)
}

a := Address{
    Street: "123",
    City:   "Unknown",
    State:  "Virginia",
    Zip:    "12345",
}

err := a.Validate()
fmt.Println(err)
// Output:
// Street: the length must be between 5 and 50; State: must be in a valid format.

Note that when calling validation.ValidateStruct to validate a struct, you should pass to the method a pointer to the struct instead of the struct itself. Similarly, when calling validation.Field to specify the rules for a struct field, you should use a pointer to the struct field.

When the struct validation is performed, the fields are validated in the order they are specified in ValidateStruct. And when each field is validated, its rules are also evaluated in the order they are associated with the field. If a rule fails, an error is recorded for that field, and the validation will continue with the next field.

View on Github

8 - Terraform-validator:

A norms and conventions validator for Terraform.

This tool will help you ensure that a terraform folder answer to your norms and conventions rules. This can be really useful in several cases :

  • You're a team that want to have a clean and maintainable code.
  • You're a lonely developer that develop a lot of modules and you want to have a certain consistency between them.

Features:

  •  make sure that the block names match a certain pattern.
  •  make sure that the code is properly dispatched. To do this you can decide what type of block can contain each file (for example output blocks must be in outputs.tf).
  •  ensure that mandatory .tf files are present.
  •  ensure that the terraform version has been defined.
  •  ensure that the providers' version has been defined.
  •  make sure that the variables and/or outputs blocks have the description argument filled in.
  •  layered terraform folders (test recursively).

⚠️ Terraform 0.12+ is supported only by the versions 2.0.0 and higher.

Documentation

Please find the full documentation here (ReadTheDocs).

View on Github

9 - Validate:

Go package for data validation and filtering. support validate Map, Struct, Request(Form, JSON, url.Values, Uploaded Files) data and more features.

validate is a generic Go data validate and filter tool library.

  • Support quick validate Map, Struct, Request(Form, JSON, url.Values, UploadedFile) data
    • Validating http.Request automatically collects data based on the request Content-Type value
    • Supports checking each child value in a slice. eg: v.StringRule("tags.*", "required|string")
  • Support filter/sanitize/convert data before validate
  • Support add custom filter/validator func
  • Support scene settings, verify different fields in different scenes
  • Support custom error messages, field translates.
    • Can use message, label tags in struct
  • Customizable i18n aware error messages, built in en, zh-CN, zh-TW
  • Built-in common data type filter/converter. see Built In Filters
  • Many commonly used validators have been built in(> 70), see Built In Validators
  • Can use validate in any frameworks, such as Gin, Echo, Chi and more
  • Supports direct use of rules to validate value. eg: validate.Val("xyz@mail.com", "required|email")

Inspired the projects albrow/forms and asaskevich/govalidator and inhere/php-validate. Thank you very much

Validate Struct

Use the validate tag of the structure, you can quickly config a structure.

Config the struct use tags

Field translations and error messages for structs can be quickly configured using the message and label tags.

  • Support configuration field mapping through structure tag, read the value of json tag by default
  • Support configuration error message via structure's message tag
  • Support configuration field translation via structure's label tag
package main

import (
	"fmt"
	"time"

	"github.com/gookit/validate"
)

// UserForm struct
type UserForm struct {
	Name     string    `validate:"required|min_len:7" message:"required:{field} is required" label:"User Name"`
	Email    string    `validate:"email" message:"email is invalid" label:"User Email"`
	Age      int       `validate:"required|int|min:1|max:99" message:"int:age must int|min:age min value is 1"`
	CreateAt int       `validate:"min:1"`
	Safe     int       `validate:"-"`
	UpdateAt time.Time `validate:"required" message:"update time is required"`
	Code     string    `validate:"customValidator"`
	// ExtInfo nested struct
	ExtInfo struct{
		Homepage string `validate:"required" label:"Home Page"`
		CityName string
	} `validate:"required" label:"Home Page"`
}

// CustomValidator custom validator in the source struct.
func (f UserForm) CustomValidator(val string) bool {
	return len(val) == 4
}

View on Github

10 - Validate:

This package provides a framework for writing validations for Go applications. It does provide you with few validators, but if you need others you can easly build them.

Installation

$ go get github.com/gobuffalo/validate

Usage

Using validate is pretty easy, just define some Validator objects and away you go.

Here is a pretty simple example:

package main

import (
	"log"

	v "github.com/gobuffalo/validate"
)

type User struct {
	Name  string
	Email string
}

func (u *User) IsValid(errors *v.Errors) {
	if u.Name == "" {
		errors.Add("name", "Name must not be blank!")
	}
	if u.Email == "" {
		errors.Add("email", "Email must not be blank!")
	}
}

func main() {
	u := User{Name: "", Email: ""}
	errors := v.Validate(&u)
	log.Println(errors.Errors)
  // map[name:[Name must not be blank!] email:[Email must not be blank!]]
}

In the previous example I wrote a single Validator for the User struct. To really get the benefit of using go-validator, as well as the Go language, I would recommend creating distinct validators for each thing you want to validate, that way they can be run concurrently.

package main

import (
	"fmt"
	"log"
	"strings"

	v "github.com/gobuffalo/validate"
)

type User struct {
	Name  string
	Email string
}

type PresenceValidator struct {
	Field string
	Value string
}

func (v *PresenceValidator) IsValid(errors *v.Errors) {
	if v.Value == "" {
		errors.Add(strings.ToLower(v.Field), fmt.Sprintf("%s must not be blank!", v.Field))
	}
}

func main() {
	u := User{Name: "", Email: ""}
	errors := v.Validate(&PresenceValidator{"Email", u.Email}, &PresenceValidator{"Name", u.Name})
	log.Println(errors.Errors)
        // map[name:[Name must not be blank!] email:[Email must not be blank!]]
}

That's really it. Pretty simple and straight-forward Just a nice clean framework for writing your own validators. Use in good health.

View on Github

Thank you for following this article.

Related videos:

Golang Microservices: Validations

#go #golang #validators 

Hertha  Mayer

Hertha Mayer

1594769515

How to validate mobile phone number in laravel with example

Data validation and sanitization is a very important thing from security point of view for a web application. We can not rely on user’s input. In this article i will let you know how to validate mobile phone number in laravel with some examples.

if we take some user’s information in our application, so usually we take phone number too. And if validation on the mobile number field is not done, a user can put anything in the mobile number field and without genuine phone number, this data would be useless.

Since we know that mobile number can not be an alpha numeric or any alphabates aand also it should be 10 digit number. So here in this examples we will add 10 digit number validation in laravel application.

We will aalso see the uses of regex in the validation of mobile number. So let’s do it with two different way in two examples.

Example 1:

In this first example we will write phone number validation in HomeController where we will processs user’s data.

<?php

namespace App\Http\Controllers;

use Illuminate\Http\Request;
use App\User;

class HomeController extends Controller
{
    /**
     * Show the application dashboard.
     *
     * @return \Illuminate\Http\Response
     */
    public function create()
    {
        return view('createUser');
    }

    /**
     * Show the application dashboard.
     *
     * @return \Illuminate\Http\Response
     */
    public function store(Request $request)
    {
        $request->validate([
                'name' => 'required',
                'phone' => 'required|digits:10',
                'email' => 'required|email|unique:users'
            ]);

        $input = $request->all();
        $user = User::create($input);

        return back()->with('success', 'User created successfully.');
    }
}

Example 2:

In this second example, we will use regex for user’s mobile phone number validation before storing user data in our database. Here, we will write the validation in Homecontroller like below.

<?php

namespace App\Http\Controllers;

use Illuminate\Http\Request;
use App\User;
use Validator;

class HomeController extends Controller
{
    /**
     * Show the application dashboard.
     *
     * @return \Illuminate\Http\Response
     */
    public function create()
    {
        return view('createUser');
    }

    /**
     * Show the application dashboard.
     *
     * @return \Illuminate\Http\Response
     */
    public function store(Request $request)
    {
        $request->validate([
                'name' => 'required',
                'phone' => 'required|regex:/^([0-9\s\-\+\(\)]*)$/|min:10',
                'email' => 'required|email|unique:users'
            ]);

        $input = $request->all();
        $user = User::create($input);

        return back()->with('success', 'User created successfully.');
    }
}

#laravel #laravel phone number validation #laravel phone validation #laravel validation example #mobile phone validation in laravel #phone validation with regex #validate mobile in laravel

Jade Bird

Jade Bird

1666770774

Variables in Python

In this Python tutorial for beginners, we learn about Variables in Python. Variables are containers for storing data values. A Python variable is a symbolic name that is a reference or pointer to an object.

Code in GitHub: https://github.com/AlexTheAnalyst/PythonYouTubeSeries/blob/main/Python%20Basics%20101%20-%20Variables.ipynb 


Creating Variables

Python has no command for declaring a variable.

A variable is created the moment you first assign a value to it.

Example

x = 5
y = "John"
print(x)
print(y)

Variables do not need to be declared with any particular type, and can even change type after they have been set.

Example

x = 4       # x is of type int
x = "Sally" # x is now of type str
print(x)

Casting

If you want to specify the data type of a variable, this can be done with casting.

Example

x = str(3)    # x will be '3'
y = int(3)    # y will be 3
z = float(3)  # z will be 3.0

Get the Type

You can get the data type of a variable with the type() function.

Example

x = 5
y = "John"
print(type(x))
print(type(y))

Single or Double Quotes?

String variables can be declared either by using single or double quotes:

Example

x = "John"
# is the same as
x = 'John'

Case-Sensitive

Variable names are case-sensitive.

Example

This will create two variables:

a = 4
A = "Sally"
#A will not overwrite a

Python Variables: How to Define/Declare String Variable Types

What is a Variable in Python?

A Python variable is a reserved memory location to store values. In other words, a variable in a python program gives data to the computer for processing.

Python Variable Types

Every value in Python has a datatype. Different data types in Python are Numbers, List, Tuple, Strings, Dictionary, etc. Variables in Python can be declared by any name or even alphabets like a, aa, abc, etc.

In this tutorial, we will learn,

  • How to Declare and use a Variable
  • Re-declare a Variable
  • Concatenate Variables
  • Local & Global Variables
  • Delete a variable

How to Declare and use a Variable

Let see an example. We will define variable in Python and declare it as “a” and print it.

a=100 
print (a)

Re-declare a Variable

You can re-declare Python variables even after you have declared once.

Here we have Python declare variable initialized to f=0.

Later, we re-assign the variable f to value “guru99”

Variables in Python

Python 2 Example

# Declare a variable and initialize it
f = 0
print f
# re-declaring the variable works
f = 'guru99'
print f

Python 3 Example

# Declare a variable and initialize it
f = 0
print(f)
# re-declaring the variable works
f = 'guru99'
print(f)

Python String Concatenation and Variable

Let’s see whether you can concatenate different data types like string and number together. For example, we will concatenate “Guru” with the number “99”.

Unlike Java, which concatenates number with string without declaring number as string, while declaring variables in Python requires declaring the number as string otherwise it will show a TypeError

Variables in Python

For the following code, you will get undefined output –

a="Guru"
b = 99
print a+b

Once the integer is declared as string, it can concatenate both “Guru” + str(“99”)= “Guru99” in the output.

a="Guru"
b = 99
print(a+str(b))

Python Variable Types: Local & Global

There are two types of variables in Python, Global variable and Local variable. When you want to use the same variable for rest of your program or module you declare it as a global variable, while if you want to use the variable in a specific function or method, you use a local variable while Python variable declaration.

Let’s understand this Python variable types with the difference between local and global variables in the below program.

  1. Let us define variable in Python where the variable “f” is global in scope and is assigned value 101 which is printed in output
  2. Variable f is again declared in function and assumes local scope. It is assigned value “I am learning Python.” which is printed out as an output. This Python declare variable is different from the global variable “f” defined earlier
  3. Once the function call is over, the local variable f is destroyed. At line 12, when we again, print the value of “f” is it displays the value of global variable f=101

Variables in Python

Python 2 Example

# Declare a variable and initialize it
f = 101
print f
# Global vs. local variables in functions
def someFunction():
# global f
    f = 'I am learning Python'
    print f
someFunction()
print f

Python 3 Example

# Declare a variable and initialize it
f = 101
print(f)
# Global vs. local variables in functions
def someFunction():
# global f
    f = 'I am learning Python'
    print(f)
someFunction()
print(f)

While Python variable declaration using the keyword global, you can reference the global variable inside a function.

  1. Variable “f” is global in scope and is assigned value 101 which is printed in output
  2. Variable f is declared using the keyword global. This is NOT a local variable, but the same global variable declared earlier. Hence when we print its value, the output is 101

We changed the value of “f” inside the function. Once the function call is over, the changed value of the variable “f” persists. At line 12, when we again, print the value of “f” is it displays the value “changing global variable”

Variables in Python

Python 2 Example

f = 101;
print f
# Global vs.local variables in functions
def someFunction():
  global f
  print f
  f = "changing global variable"
someFunction()
print f

Python 3 Example

f = 101;
print(f)
# Global vs.local variables in functions
def someFunction():
  global f
  print(f)
  f = "changing global variable"
someFunction()
print(f)

Delete a variable

You can also delete Python variables using the command del “variable name”.

In the below example of Python delete variable, we deleted variable f, and when we proceed to print it, we get error “variable name is not defined” which means you have deleted the variable.

Variables in Python

Example of Python delete variable or Python clear variable :

f = 11;
print(f)
del f
print(f)

Summary:

  • Variables are referred to “envelop” or “buckets” where information can be maintained and referenced. Like any other programming language Python also uses a variable to store the information.
  • Variables can be declared by any name or even alphabets like a, aa, abc, etc.
  • Variables can be re-declared even after you have declared them for once
  • Python constants can be understood as types of variables that hold the value which can not be changed. Usually Python constants are referenced from other files. Python define constant is declared in a new or separate file which contains functions, modules, etc.
  • Types of variables in Python or Python variable types : Local & Global
  • Declare local variable when you want to use it for current function
  • Declare Global variable when you want to use the same variable for rest of the program

To delete a variable, it uses keyword “del”.


A Beginner’s Guide To Python Variables

A variable is a fundamental concept in any programming language. It is a reserved memory location that stores and manipulates data. This tutorial on Python variables will help you learn more about what they are, the different data types of variables, the rules for naming variables in Python. You will also perform some basic operations on numbers and strings. We’ll use Jupyter Notebook to implement the Python codes.

Variables are entities of a program that holds a value. Here is an example of a variable:

x=100 

In the below diagram, the box holds a value of 100 and is named as x. Therefore, the variable is x, and the data it holds is the value.

xvariable

The data type for a variable is the type of data it holds. 

In the above example, x is holding 100, which is a number, and the data type of x is a number.

In Python, there are three types of numbers: Integer, Float, and Complex.

Integers are numbers without decimal points. Floats are numbers with decimal points. Complex numbers have real parts and imaginary parts.

Another data type that is very different from a number is called a string, which is a collection of characters.

Let’s see a variable with an integer data type:

x=100

To check the data type of x, use the type() function:

type(x)

type-x

Python allows you to assign variables while performing arithmetic operations.

x=654*6734
type(x)

x-int

To display the output of the variable, use the print() function.

print(x) #It gives the product of the two numbers

Now, let’s see an example of a floating-point number:

x=3.14
print(x)

type(x) #Here the type the variable is float

float

Strings are declared within a single or double quote.

x=’Simplilearn’

print(x)

x=” Simplilearn.”

print(x)

type(x)
x-simplilearn

In all of the examples above, we only assigned a single value to the variables. Python has specific data types or objects that hold a collection of values, too. A Python List is one such example.

Here is an example of a list:

x=[14,67,9]

print(x)

type(x)
x-list

You can extract the values from the list using the index position method. In lists, the first element index position starts at zero, the second element at one, the third element at two, and so on.

To extract the first element from the list x:

print(x[0])

print-x

To extract the third element from the list x:

print(x[2])

Lists are mutable objects, which means you can change the values in a list once they are declared.

x[2]=70 #Reassigning the third element in the list to 70

print(x)
print-x-2

Earlier, the elements in the list had [14, 67, 9]. Now, they have [14, 67, 70].

Tuples are a type of Python object that holds a collection of value, which is ordered and immutable. Unlike a list that uses a square bracket, tuples use parentheses.

x=(4,8,6)

print(x)

type(x)
print-x-3

Similar to lists, tuples can also be extracted with the index position method.

print(x[1]) #Give the element present at index 1, i.e. 8

If you want to change any value in a tuple, it will throw an error. Once you have stored the values in a variable for a tuple, it remains the same.

tuple

When we deal with files, we need a variable that points to it, called file pointers. The advantage of having file pointers is that when you need to perform various operations on a file, instead of providing the file’s entire path location or name every time, you can assign it to a particular variable and use that instead.

Here is how you can assign a variable to a file:

x=open(‘C:/Users/Simplilearn/Downloads/JupyterNotebook.ipynb’,’r’) 

type(x)
x-open

Suppose you want to assign values to multiple variables. Instead of having multiple lines of code for each variable, you can assign it in a single line of code.

(x, y, z)=5, 10, 5

xyyz

The following line code results in an error because the number of values assigned doesn’t match with the number of variables declared.

value-error

If you want to assign the same value to multiple variables, use the following syntax:

x=y=z=1

xyz-1

Now, let's look at the various rules for naming a variable.

1. A variable name must begin with a letter of the alphabet or an underscore(_)

Example:

abc=100 #valid syntax

    _abc=100 #valid syntax

    3a=10 #invalid syntax

    @abc=10 #invalid syntax

. The first character can be followed by letters, numbers or underscores.

Example:

a100=100 #valid

    _a984_=100 #valid

    a9967$=100 #invalid

    xyz-2=100 #invalid

Python variable names are case sensitive.

Example:

a100 is different from A100.

    a100=100

  A100=200
print-a

Reserved words cannot be used as variable names.

Example:

break, class, try, continue, while, if

break=10

class=5

try=100
break-ten

Python is more effective and more comfortable to perform when you use arithmetic operations.

The following is an example of adding the values of two variables and storing them in a third variable:

x=20

y=10

result=x+y

print(result)
x-20

Similarly, we can perform subtraction as well.

result=x-y

print(result)

result-x-y

Additionally, to perform multiplication and division, try the following lines of code:

result=x*y

print(result)

result=x/y

print(result)

result-print-result

As you can see, in the case of division, the result is not an integer, but a float value. To get the result of the division in integers, use “//”the integer division.

The division of two numbers gives you the quotient. To get the remainder, use the modulo (%) operator.

modulo

Now that we know how to perform arithmetic operations on numbers let us look at some operations that can be performed on string variables.

var = ‘Simplilearn’

You can extract each character from the variable using the index position. Similar to lists and tuples, the first element position starts at index zero, the second element index at one, and so on.

print(var[0]) #Gives the character at index 0, i.e. S

print(var[4]) #Gives the character at index 4, i.e. l

var-simplilearn

If you want to extract a range of characters from the string variable, you can use a colon (:) and provide the range between the ones you want to receive values from. The last index is always excluded. Therefore, you should always provide one plus the number of characters you want to fetch. 

print(var[0:3]) #This will extract the first three characters from zero, first, and second index.

The same operation can be performed by excluding the starting index.

print(var[:3])

print-sim

The following example prints the values from the fifth location until the end of the string.

print-ilearn

Let’s see what happens when you try to print the following:

print(var[0:20]) #Prints the entire string, although the string does not have 20 characters.

var-simplilearn-print

To print the length of a string, use the len() function.

len(var)

len-var

Let’s see how you can extract characters from two strings and generate a new string.

var1 = “It’s Sunday”

var2 = “Have a great day”

The new string should say, “It’s a great Sunday” and be stored in var3.

var3 = var1[:5] + var2[5:13] + var1[5:]

print(var3)

great-sunday

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Conclusion

I hope this blog helped you learn the concepts of Python variables. After reading this blog, you may have learned more about what a variable is, rules for declaring a variable, how to perform arithmetic operations on variables, and how to extract elements from numeric and string variables using the index position.

#python #programming 

How to Create Arrays in Python

In this tutorial, you'll know the basics of how to create arrays in Python using the array module. Learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

This tutorialvideo on 'Arrays in Python' will help you establish a strong hold on all the fundamentals in python programming language. Below are the topics covered in this video:  
1:15 What is an array?
2:53 Is python list same as an array?
3:48  How to create arrays in python?
7:19 Accessing array elements
9:59 Basic array operations
        - 10:33  Finding the length of an array
        - 11:44  Adding Elements
        - 15:06  Removing elements
        - 18:32  Array concatenation
       - 20:59  Slicing
       - 23:26  Looping  


Python Array Tutorial – Define, Index, Methods

In this article, you'll learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

The artcile covers arrays that you create by importing the array module. We won't cover NumPy arrays here.

Table of Contents

  1. Introduction to Arrays
    1. The differences between Lists and Arrays
    2. When to use arrays
  2. How to use arrays
    1. Define arrays
    2. Find the length of arrays
    3. Array indexing
    4. Search through arrays
    5. Loop through arrays
    6. Slice an array
  3. Array methods for performing operations
    1. Change an existing value
    2. Add a new value
    3. Remove a value
  4. Conclusion

Let's get started!

What are Python Arrays?

Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.

Specifically, they are an ordered collection of elements with every value being of the same data type. That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type.

What's the Difference between Python Lists and Python Arrays?

Lists are one of the most common data structures in Python, and a core part of the language.

Lists and arrays behave similarly.

Just like arrays, lists are an ordered sequence of elements.

They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.

However, lists and arrays are not the same thing.

Lists store items that are of various data types. This means that a list can contain integers, floating point numbers, strings, or any other Python data type, at the same time. That is not the case with arrays.

As mentioned in the section above, arrays store only items that are of the same single data type. There are arrays that contain only integers, or only floating point numbers, or only any other Python data type you want to use.

When to Use Python Arrays

Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the array module in order to be used.

Arrays of the array module are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.

They are also more compact and take up less memory and space which makes them more size efficient compared to lists.

If you want to perform mathematical calculations, then you should use NumPy arrays by importing the NumPy package. Besides that, you should just use Python arrays when you really need to, as lists work in a similar way and are more flexible to work with.

How to Use Arrays in Python

In order to create Python arrays, you'll first have to import the array module which contains all the necassary functions.

There are three ways you can import the array module:

  • By using import array at the top of the file. This includes the module array. You would then go on to create an array using array.array().
import array

#how you would create an array
array.array()
  • Instead of having to type array.array() all the time, you could use import array as arr at the top of the file, instead of import array alone. You would then create an array by typing arr.array(). The arr acts as an alias name, with the array constructor then immediately following it.
import array as arr

#how you would create an array
arr.array()
  • Lastly, you could also use from array import *, with * importing all the functionalities available. You would then create an array by writing the array() constructor alone.
from array import *

#how you would create an array
array()

How to Define Arrays in Python

Once you've imported the array module, you can then go on to define a Python array.

The general syntax for creating an array looks like this:

variable_name = array(typecode,[elements])

Let's break it down:

  • variable_name would be the name of the array.
  • The typecode specifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type.
  • Inside square brackets you mention the elements that would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writing variable_name = array(typecode) alone, without any elements.

Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:

TYPECODEC TYPEPYTHON TYPESIZE
'b'signed charint1
'B'unsigned charint1
'u'wchar_tUnicode character2
'h'signed shortint2
'H'unsigned shortint2
'i'signed intint2
'I'unsigned intint2
'l'signed longint4
'L'unsigned longint4
'q'signed long longint8
'Q'unsigned long longint8
'f'floatfloat4
'd'doublefloat8

Tying everything together, here is an example of how you would define an array in Python:

import array as arr 

numbers = arr.array('i',[10,20,30])


print(numbers)

#output

#array('i', [10, 20, 30])

Let's break it down:

  • First we included the array module, in this case with import array as arr .
  • Then, we created a numbers array.
  • We used arr.array() because of import array as arr .
  • Inside the array() constructor, we first included i, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, with H for example, would mean that no negative values are allowed.
  • Lastly, we included the values to be stored in the array in square brackets.

Keep in mind that if you tried to include values that were not of i typecode, meaning they were not integer values, you would get an error:

import array as arr 

numbers = arr.array('i',[10.0,20,30])


print(numbers)

#output

#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 14, in <module>
#   numbers = arr.array('i',[10.0,20,30])
#TypeError: 'float' object cannot be interpreted as an integer

In the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.

Another way to create an array is the following:

from array import *

#an array of floating point values
numbers = array('d',[10.0,20.0,30.0])

print(numbers)

#output

#array('d', [10.0, 20.0, 30.0])

The example above imported the array module via from array import * and created an array numbers of float data type. This means that it holds only floating point numbers, which is specified with the 'd' typecode.

How to Find the Length of an Array in Python

To find out the exact number of elements contained in an array, use the built-in len() method.

It will return the integer number that is equal to the total number of elements in the array you specify.

import array as arr 

numbers = arr.array('i',[10,20,30])


print(len(numbers))

#output
# 3

In the example above, the array contained three elements – 10, 20, 30 – so the length of numbers is 3.

Array Indexing and How to Access Individual Items in an Array in Python

Each item in an array has a specific address. Individual items are accessed by referencing their index number.

Indexing in Python, and in all programming languages and computing in general, starts at 0. It is important to remember that counting starts at 0 and not at 1.

To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.

The general syntax would look something like this:

array_name[index_value_of_item]

Here is how you would access each individual element in an array:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[0]) # gets the 1st element
print(numbers[1]) # gets the 2nd element
print(numbers[2]) # gets the 3rd element

#output

#10
#20
#30

Remember that the index value of the last element of an array is always one less than the length of the array. Where n is the length of the array, n - 1 will be the index value of the last item.

Note that you can also access each individual element using negative indexing.

With negative indexing, the last element would have an index of -1, the second to last element would have an index of -2, and so on.

Here is how you would get each item in an array using that method:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[-1]) #gets last item
print(numbers[-2]) #gets second to last item
print(numbers[-3]) #gets first item
 
#output

#30
#20
#10

How to Search Through an Array in Python

You can find out an element's index number by using the index() method.

You pass the value of the element being searched as the argument to the method, and the element's index number is returned.

import array as arr 

numbers = arr.array('i',[10,20,30])

#search for the index of the value 10
print(numbers.index(10))

#output

#0

If there is more than one element with the same value, the index of the first instance of the value will be returned:

import array as arr 


numbers = arr.array('i',[10,20,30,10,20,30])

#search for the index of the value 10
#will return the index number of the first instance of the value 10
print(numbers.index(10))

#output

#0

How to Loop through an Array in Python

You've seen how to access each individual element in an array and print it out on its own.

You've also seen how to print the array, using the print() method. That method gives the following result:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers)

#output

#array('i', [10, 20, 30])

What if you want to print each value one by one?

This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.

For this you can use a simple for loop:

import array as arr 

numbers = arr.array('i',[10,20,30])

for number in numbers:
    print(number)
    
#output
#10
#20
#30

You could also use the range() function, and pass the len() method as its parameter. This would give the same result as above:

import array as arr  

values = arr.array('i',[10,20,30])

#prints each individual value in the array
for value in range(len(values)):
    print(values[value])

#output

#10
#20
#30

How to Slice an Array in Python

To access a specific range of values inside the array, use the slicing operator, which is a colon :.

When using the slicing operator and you only include one value, the counting starts from 0 by default. It gets the first item, and goes up to but not including the index number you specify.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#get the values 10 and 20 only
print(numbers[:2])  #first to second position

#output

#array('i', [10, 20])

When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])


#get the values 20 and 30 only
print(numbers[1:3]) #second to third position

#output

#rray('i', [20, 30])

Methods For Performing Operations on Arrays in Python

Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.

Let's see some of the most commonly used methods which are used for performing operations on arrays.

How to Change the Value of an Item in an Array

You can change the value of a specific element by speficying its position and assigning it a new value:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#change the first element
#change it from having a value of 10 to having a value of 40
numbers[0] = 40

print(numbers)

#output

#array('i', [40, 20, 30])

How to Add a New Value to an Array

To add one single value at the end of an array, use the append() method:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40)

print(numbers)

#output

#array('i', [10, 20, 30, 40])

Be aware that the new item you add needs to be the same data type as the rest of the items in the array.

Look what happens when I try to add a float to an array of integers:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40.0)

print(numbers)

#output

#Traceback (most recent call last):
#  File "/Users/dionysialemonaki/python_articles/demo.py", line 19, in <module>
#   numbers.append(40.0)
#TypeError: 'float' object cannot be interpreted as an integer

But what if you want to add more than one value to the end an array?

Use the extend() method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integers 40,50,60 to the end of numbers
#The numbers need to be enclosed in square brackets

numbers.extend([40,50,60])

print(numbers)

#output

#array('i', [10, 20, 30, 40, 50, 60])

And what if you don't want to add an item to the end of an array? Use the insert() method, to add an item at a specific position.

The insert() function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 in the first position
#remember indexing starts at 0

numbers.insert(0,40)

print(numbers)

#output

#array('i', [40, 10, 20, 30])

How to Remove a Value from an Array

To remove an element from an array, use the remove() method and include the value as an argument to the method.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30])

With remove(), only the first instance of the value you pass as an argument will be removed.

See what happens when there are more than one identical values:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Only the first occurence of 10 is removed.

You can also use the pop() method, and specify the position of the element to be removed:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

#remove the first instance of 10
numbers.pop(0)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Conclusion

And there you have it - you now know the basics of how to create arrays in Python using the array module. Hopefully you found this guide helpful.

Thanks for reading and happy coding!

#python #programming 

Connor Mills

Connor Mills

1670560264

Understanding Arrays in Python

Learn how to use Python arrays. Create arrays in Python using the array module. You'll see how to define them and the different methods commonly used for performing operations on them.
 

The artcile covers arrays that you create by importing the array module. We won't cover NumPy arrays here.

Table of Contents

  1. Introduction to Arrays
    1. The differences between Lists and Arrays
    2. When to use arrays
  2. How to use arrays
    1. Define arrays
    2. Find the length of arrays
    3. Array indexing
    4. Search through arrays
    5. Loop through arrays
    6. Slice an array
  3. Array methods for performing operations
    1. Change an existing value
    2. Add a new value
    3. Remove a value
  4. Conclusion

Let's get started!


What are Python Arrays?

Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.

Specifically, they are an ordered collection of elements with every value being of the same data type. That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type.

What's the Difference between Python Lists and Python Arrays?

Lists are one of the most common data structures in Python, and a core part of the language.

Lists and arrays behave similarly.

Just like arrays, lists are an ordered sequence of elements.

They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.

However, lists and arrays are not the same thing.

Lists store items that are of various data types. This means that a list can contain integers, floating point numbers, strings, or any other Python data type, at the same time. That is not the case with arrays.

As mentioned in the section above, arrays store only items that are of the same single data type. There are arrays that contain only integers, or only floating point numbers, or only any other Python data type you want to use.

When to Use Python Arrays

Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the array module in order to be used.

Arrays of the array module are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.

They are also more compact and take up less memory and space which makes them more size efficient compared to lists.

If you want to perform mathematical calculations, then you should use NumPy arrays by importing the NumPy package. Besides that, you should just use Python arrays when you really need to, as lists work in a similar way and are more flexible to work with.

How to Use Arrays in Python

In order to create Python arrays, you'll first have to import the array module which contains all the necassary functions.

There are three ways you can import the array module:

  1. By using import array at the top of the file. This includes the module array. You would then go on to create an array using array.array().
import array

#how you would create an array
array.array()
  1. Instead of having to type array.array() all the time, you could use import array as arr at the top of the file, instead of import array alone. You would then create an array by typing arr.array(). The arr acts as an alias name, with the array constructor then immediately following it.
import array as arr

#how you would create an array
arr.array()
  1. Lastly, you could also use from array import *, with * importing all the functionalities available. You would then create an array by writing the array() constructor alone.
from array import *

#how you would create an array
array()

How to Define Arrays in Python

Once you've imported the array module, you can then go on to define a Python array.

The general syntax for creating an array looks like this:

variable_name = array(typecode,[elements])

Let's break it down:

  • variable_name would be the name of the array.
  • The typecode specifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type.
  • Inside square brackets you mention the elements that would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writing variable_name = array(typecode) alone, without any elements.

Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:

TYPECODEC TYPEPYTHON TYPESIZE
'b'signed charint1
'B'unsigned charint1
'u'wchar_tUnicode character2
'h'signed shortint2
'H'unsigned shortint2
'i'signed intint2
'I'unsigned intint2
'l'signed longint4
'L'unsigned longint4
'q'signed long longint8
'Q'unsigned long longint8
'f'floatfloat4
'd'doublefloat8

Tying everything together, here is an example of how you would define an array in Python:

import array as arr 

numbers = arr.array('i',[10,20,30])


print(numbers)

#output

#array('i', [10, 20, 30])

Let's break it down:

  • First we included the array module, in this case with import array as arr .
  • Then, we created a numbers array.
  • We used arr.array() because of import array as arr .
  • Inside the array() constructor, we first included i, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, with H for example, would mean that no negative values are allowed.
  • Lastly, we included the values to be stored in the array in square brackets.

Keep in mind that if you tried to include values that were not of i typecode, meaning they were not integer values, you would get an error:

import array as arr 

numbers = arr.array('i',[10.0,20,30])


print(numbers)

#output

#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 14, in <module>
#   numbers = arr.array('i',[10.0,20,30])
#TypeError: 'float' object cannot be interpreted as an integer

In the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.

Another way to create an array is the following:

from array import *

#an array of floating point values
numbers = array('d',[10.0,20.0,30.0])

print(numbers)

#output

#array('d', [10.0, 20.0, 30.0])

The example above imported the array module via from array import * and created an array numbers of float data type. This means that it holds only floating point numbers, which is specified with the 'd' typecode.

How to Find the Length of an Array in Python

To find out the exact number of elements contained in an array, use the built-in len() method.

It will return the integer number that is equal to the total number of elements in the array you specify.

import array as arr 

numbers = arr.array('i',[10,20,30])


print(len(numbers))

#output
# 3

In the example above, the array contained three elements – 10, 20, 30 – so the length of numbers is 3.

Array Indexing and How to Access Individual Items in an Array in Python

Each item in an array has a specific address. Individual items are accessed by referencing their index number.

Indexing in Python, and in all programming languages and computing in general, starts at 0. It is important to remember that counting starts at 0 and not at 1.

To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.

The general syntax would look something like this:

array_name[index_value_of_item]

Here is how you would access each individual element in an array:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[0]) # gets the 1st element
print(numbers[1]) # gets the 2nd element
print(numbers[2]) # gets the 3rd element

#output

#10
#20
#30

Remember that the index value of the last element of an array is always one less than the length of the array. Where n is the length of the array, n - 1 will be the index value of the last item.

Note that you can also access each individual element using negative indexing.

With negative indexing, the last element would have an index of -1, the second to last element would have an index of -2, and so on.

Here is how you would get each item in an array using that method:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[-1]) #gets last item
print(numbers[-2]) #gets second to last item
print(numbers[-3]) #gets first item
 
#output

#30
#20
#10

How to Search Through an Array in Python

You can find out an element's index number by using the index() method.

You pass the value of the element being searched as the argument to the method, and the element's index number is returned.

import array as arr 

numbers = arr.array('i',[10,20,30])

#search for the index of the value 10
print(numbers.index(10))

#output

#0

If there is more than one element with the same value, the index of the first instance of the value will be returned:

import array as arr 


numbers = arr.array('i',[10,20,30,10,20,30])

#search for the index of the value 10
#will return the index number of the first instance of the value 10
print(numbers.index(10))

#output

#0

How to Loop through an Array in Python

You've seen how to access each individual element in an array and print it out on its own.

You've also seen how to print the array, using the print() method. That method gives the following result:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers)

#output

#array('i', [10, 20, 30])

What if you want to print each value one by one?

This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.

For this you can use a simple for loop:

import array as arr 

numbers = arr.array('i',[10,20,30])

for number in numbers:
    print(number)
    
#output
#10
#20
#30

You could also use the range() function, and pass the len() method as its parameter. This would give the same result as above:

import array as arr  

values = arr.array('i',[10,20,30])

#prints each individual value in the array
for value in range(len(values)):
    print(values[value])

#output

#10
#20
#30

How to Slice an Array in Python

To access a specific range of values inside the array, use the slicing operator, which is a colon :.

When using the slicing operator and you only include one value, the counting starts from 0 by default. It gets the first item, and goes up to but not including the index number you specify.


import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#get the values 10 and 20 only
print(numbers[:2])  #first to second position

#output

#array('i', [10, 20])

When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])


#get the values 20 and 30 only
print(numbers[1:3]) #second to third position

#output

#rray('i', [20, 30])

Methods For Performing Operations on Arrays in Python

Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.

Let's see some of the most commonly used methods which are used for performing operations on arrays.

How to Change the Value of an Item in an Array

You can change the value of a specific element by speficying its position and assigning it a new value:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#change the first element
#change it from having a value of 10 to having a value of 40
numbers[0] = 40

print(numbers)

#output

#array('i', [40, 20, 30])

How to Add a New Value to an Array

To add one single value at the end of an array, use the append() method:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40)

print(numbers)

#output

#array('i', [10, 20, 30, 40])

Be aware that the new item you add needs to be the same data type as the rest of the items in the array.

Look what happens when I try to add a float to an array of integers:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40.0)

print(numbers)

#output

#Traceback (most recent call last):
#  File "/Users/dionysialemonaki/python_articles/demo.py", line 19, in <module>
#   numbers.append(40.0)
#TypeError: 'float' object cannot be interpreted as an integer

But what if you want to add more than one value to the end an array?

Use the extend() method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integers 40,50,60 to the end of numbers
#The numbers need to be enclosed in square brackets

numbers.extend([40,50,60])

print(numbers)

#output

#array('i', [10, 20, 30, 40, 50, 60])

And what if you don't want to add an item to the end of an array? Use the insert() method, to add an item at a specific position.

The insert() function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 in the first position
#remember indexing starts at 0

numbers.insert(0,40)

print(numbers)

#output

#array('i', [40, 10, 20, 30])

How to Remove a Value from an Array

To remove an element from an array, use the remove() method and include the value as an argument to the method.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30])

With remove(), only the first instance of the value you pass as an argument will be removed.

See what happens when there are more than one identical values:


import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Only the first occurence of 10 is removed.

You can also use the pop() method, and specify the position of the element to be removed:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

#remove the first instance of 10
numbers.pop(0)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Conclusion

And there you have it - you now know the basics of how to create arrays in Python using the array module. Hopefully you found this guide helpful.

You'll start from the basics and learn in an interacitve and beginner-friendly way. You'll also build five projects at the end to put into practice and help reinforce what you learned.

Thanks for reading and happy coding!

Original article source at https://www.freecodecamp.org

#python