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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']
Last updated on by roll