Schema Guide

The Table Schema is a core Frictionless Data concept meaning a metadata information regarding tabular data source. You can read Table Schema Spec for more information.

Creating Schema

Let's create a table schema:

Python

from frictionless import Schema, describe

schema = describe('data/table.csv', type='schema') # from a resource path

schema = Schema('data/schema.json') # from a descriptor path

schema = Schema({'fields': {'name': 'id', 'type': 'integer'}}) # from a descriptor

As you can see it's possible to create a schema providing different kinds of sources which will be detector to have some type automatically (e.g. whether it's a dict or a path). It's possible to make this step more explicit:

Python

from frictionless import Schema, Field

schema = Schema(fields=[Field(name='id', type='string')]) # from fields

schema = Schema(descriptor='data/schema.json') # from a descriptor

Describing Schema

The specs support some additional schema's metadata:

Python

from frictionless import Schema, Resource

package = Schema(

fields=[Field(name='id', type='string')],

missing_values=['na'],

primary_key=['id'],

# foreign_keys

)

If you have created a schema, for example, from a descriptor you can access this properties:

Python

from frictionless import Schema

schema = Schema('data/schema.json')

schema.missing_values

schema.primary_key

# and others

And edit them:

Python

from frictionless import Schema

schema = Schema('data/schema.json')

schema.missing_values.append('-')

# and others

Field Management

The Schema class provides useful methods to manage fields:

Python

from frictionless import Schema, Field

schema = Schema('data/schema.json')

print(schema.fields)

print(schema.field_names)

schema.add_field(Field(name='name', type='string'))

field = schema.get_field('name')

print(schema.has_field('name'))

schema.remove_field('name')

[{'name': 'id', 'type': 'integer'}, {'name': 'name', 'type': 'string'}]

['id', 'name']

True

Saving Descriptor

As any of the Metadata classes the Schema class can be saved as JSON or YAML:

Python

from frictionless import Schema

schema = Schema(fields=[Field(name='id', type='integer')])

schema.to_json('schema.json') # Save as JSON

schema.to_yaml('schema.yaml') # Save as YAML

Reading Cells

During the process of data reading a resource uses a schema to convert data:

Python

from frictionless import Schema, Field

schema = Schema(fields=[Field(type='integer'), Field(type='string')])

schema.read_cells(['3', 'value']) # [3, 'value']

Writing Cells

During the process of data writing a resource uses a schema to convert data:

Python

from frictionless import Schema, Field

schema = Schema(fields=[Field(type='integer'), Field(type='string')])

schema.write_cells([3, 'value']) # ['3', 'value']

schema.write_cells([3, 'value'], types=['string']) # [3, 'value']