A Go library for working with Data Packages.
$ go get -u github.com/frictionlessdata/datapackage-go/...A data package is a collection of resources. The datapackage.Package provides various capabilities like loading local or remote data package, saving a data package descriptor and many more.
Consider we have some local csv file and a JSON descriptor in a data directory:
data/population.csv
city,year,population
london,2017,8780000
paris,2017,2240000
rome,2017,2860000
data/datapackage.json
{
"name": "world",
"resources": [
{
"name": "population",
"path": "population.csv",
"profile":"tabular-data-resource",
"schema": {
"fields": [
{"name": "city", "type": "string"},
{"name": "year", "type": "integer"},
{"name": "population", "type": "integer"}
]
}
}
]
}Let's create a data package based on this data using the datapackage.Package class:
pkg, err := datapackage.Load("data/datapackage.json")
// Check error.Once the data package is loaded, we could use the datapackage.Resource class to read data resource's contents:
resource := pkg.GetResource("population")
contents, _ := resource.ReadAll()
fmt.Println(contents)
// [[london 2017 8780000] [paris 2017 2240000] [rome 20172860000]]Or you could cast to Go types, making it easier to perform further processing:
type Population struct {
City string `tableheader:"city"`
Year string `tableheader:"year"`
Population int `tableheader:"population"`
}
var cities []Population
resource.Cast(&cities, csv.LoadHeaders())
fmt.Printf("+v", cities)
// [{City:london Year:2017 Population:8780000} {City:paris Year:2017 Population:2240000} {City:rome Year:2017 Population:2860000}]If the data is to big to be loaded at once or if you would like to perform line-by-line processing, you could iterate through the resource contents:
iter, _ := resource.Iter(csv.LoadHeaders())
sch, _ := resource.GetSchema()
for iter.Next() {
var p Population
sch.CastRow(iter.Row(), &cp)
fmt.Printf("%+v\n", p)
}
// {City:london Year:2017 Population:8780000
// {City:paris Year:2017 Population:2240000}
// {City:rome Year:2017 Population:2860000}]Or you might want to process specific columns, for instance to perform an statical analysis:
var population []float64
resource.CastColumn("population", &population, csv.LoadHeaders())
fmt.Println(ages)
// Output: [8780000 2240000 2860000]It is very common to store the data in zip bundles containing the descriptor and data files. Those are natively supported by our the datapackage.Load method. For example, lets say we have the following package.zip bundle:
|- package.zip
|- datapackage.json
|- data.csv
We could load this package by simply:
pkg, err := datapackage.Load("package.zip")
// Check error.And the library will unzip the package contents to a temporary directory and wire everything up for us.
A complete example can be found here.
You could also easily create a zip file containing the descriptor and all the data resources. Let's say you have a datapackage.Package instance, to create a zip file containing all resources simply:
err := pkg.Zip("package.zip")
// Check error.This call also download remote resources. A complete example can be found here
Basic support for configuring CSV dialect has been added. In particular delimiter, skipInitialSpace and header fields are supported. For instance, lets assume the population file has a different field delimiter:
data/population.csv
city,year,population
london;2017;8780000
paris;2017;2240000
rome;2017;2860000
One could easily parse by adding following dialect property to the world resource:
"dialect":{
"delimiter":";"
}A complete example can be found here.
Sometimes you have data scattered across many local or remote files. Datapackage-go offers an easy way you to deal all those file as one big
file. We call it multipart resources. To use this feature, simply list your files in the path property of the resource. For example, lets
say our population data is now split between north and south hemispheres. To deal with this, we only need change to change the package descriptor:
data/datapackage.json
{
"name": "world",
"resources": [
{
"name": "population",
"path": ["north.csv","south.csv"],
"profile":"tabular-data-resource",
"schema": {
"fields": [
{"name": "city", "type": "string"},
{"name": "year", "type": "integer"},
{"name": "population", "type": "integer"}
]
}
}
]
}And all the rest of the code would still be working.
A complete example can be found here.
A Data package is a container format used to describe and package a collection of data. Even though there is additional support for dealing with tabular resources, it can be used to package any kind of data.
For instance, lets say an user needs to load JSON-LD information along with some tabular data (for more on this use case, please take a look at this issue). That can be packed together in a data package descriptor:
{
"name": "carp-lake",
"title": "Carp Lake Title",
"description": "Tephra and Lithology from Carp Lake",
"resources": [
{
"name":"data",
"path": "data/carpLakeCoreStratigraphy.csv",
"format": "csv",
"schema": {
"fields": [
{"name": "depth", "type": "number"},
{"name": "notes", "type": "text"},
{"name": "core_segments", "type": "text"}
]
}
},
{
"name": "schemaorg",
"path": "data/schemaorg-ld.json",
"format": "application/ld+json"
}
]
}The package loading proceeds as usual.
pkg, err := datapackage.Load("data/datapackage.json")
// Check error.Once the data package is loaded, we could use the Resource.RawRead method to access schemaorg resource contents as a byte slice.
so := pkg.GetResource("schemaorg")
rc, _ := so.RawRead()
defer rc.Close()
contents, _ := ioutil.ReadAll(rc)
// Use contents. For instance, one could validate the JSON-LD schema and unmarshal it into a data structure.
data := pkg.GetResource("data")
dataContents, err := data.ReadAll()
// As data is a tabular resource, its content can be loaded as [][]string.The datapackage-go library also makes it easy to save packages. Let's say you're creating a program that produces data packages and would like to add or remove resource:
descriptor := map[string]interface{}{
"resources": []interface{}{
map[string]interface{}{
"name": "books",
"path": "books.csv",
"format": "csv",
"profile": "tabular-data-resource",
"schema": map[string]interface{}{
"fields": []interface{}{
map[string]interface{}{"name": "author", "type": "string"},
map[string]interface{}{"name": "title", "type": "string"},
map[string]interface{}{"name": "year", "type": "integer"},
},
},
},
},
}
pkg, err := datapackage.New(descriptor, ".", validator.InMemoryLoader())
if err != nil {
panic(err)
}
// Removing resource.
pkg.RemoveResource("books")
// Adding new resource.
pkg.AddResource(map[string]interface{}{
"name": "cities",
"path": "cities.csv",
"format": "csv",
"profile": "tabular-data-resource",
"schema": map[string]interface{}{
"fields": []interface{}{
map[string]interface{}{"name": "city", "type": "string"},
map[string]interface{}{"name": "year", "type": "integer"},
map[string]interface{}{"name": "population", "type": "integer"}
},
},
})
// Printing resource contents.
cities, _ := pkg.GetResource("cities").ReadAll()
fmt.Println(cities)
// [[london 2017 8780000] [paris 2017 2240000] [rome 20172860000]]