A utility library for working with Table Schema in Python
Project description
A library for working with Table Schema in Python.
Features
Table to work with data tables described by Table Schema
Schema representing Table Schema
Field representing Table Schema field
validate to validate Table Schema
infer to infer Table Schema from data
built-in command-line interface to validate and infer schemas
storage/plugins system to connect tables to different storage backends like SQL Database
Important Notes
There are BREAKING changes in v1 (pre-release):
package on PyPi has been renamed to tableschema
following deprecated API has been removed the package:
tableschema.push/pull_resource (use tableschema.Table)
tableschema.Validator (use tableschema.validate)
tableschema.storage (use tableschema.Storage)
tableschema.model (use tableschema.Schema)
tableschema.types (use tableschema.Field)
rebased on Table Schema v1 null/types/constraints symantics
Field.cast/test_value now accepts constraints=bool/list argument instead of skip_constraints=bool and constraint=str
other changes could be introduced before final release
documentation for previous release (v0.10) could be found here
There are deprecating changes in v0.7:
renewed API has been introduced in non breaking manner
documentation for deprecated API could be found here
Gettings Started
Installation
$ pip install jsontableschema # v0.10
$ pip install tableschema --pre # v1.0-alphaExample
from tableschema import Table
# Create table
table = Table('path.csv', schema='schema.json')
# Print schema descriptor
print(table.schema.descriptor)
# Print cast rows in a dict form
for keyed_row in table.iter(keyed=True):
print(keyed_row)Table
Table represents data described by Table Schema:
# pip install sqlalchemy tableschema-sql
import sqlalchemy as sa
from pprint import pprint
from tableschema import Table
# Data source
SOURCE = 'https://raw.githubusercontent.com/frictionlessdata/tableschema-py/master/data/data_infer.csv'
# Create SQL database
db = sa.create_engine('sqlite://')
# Data processor
def skip_under_30(erows):
for number, headers, row in erows:
krow = dict(zip(headers, row))
if krow['age'] >= 30:
yield (number, headers, row)
# Work with table
table = Table(SOURCE, post_cast=[skip_under_30])
table.schema.save('tmp/persons.json') # Save INFERRED schema
table.save('persons', backend='sql', engine=db) # Save data to SQL
table.save('tmp/persons.csv') # Save data to DRIVE
# Check the result
pprint(Table('persons', backend='sql', engine=db).read(keyed=True))
pprint(Table('tmp/persons.csv').read(keyed=True))
# Will print (twice)
# [{'age': 39, 'id': 1, 'name': 'Paul'},
# {'age': 36, 'id': 3, 'name': 'Jane'}]Schema
A model of a schema with helpful methods for working with the schema and supported data. Schema instances can be initialized with a schema source as a filepath or url to a JSON file, or a Python dict. The schema is initially validated (see validate below), and will raise an exception if not a valid Table Schema.
from tableschema import Schema
# Init schema
schema = Schema('path.json')
# Cast a row
schema.cast_row(['12345', 'a string', 'another field'])Methods available to Schema instances:
descriptor - return schema descriptor
fields - an array of the schema’s Field instances
headers - an array of the schema headers
primary_key - the primary key field for the schema as an array
foreignKey - the foreign key property for the schema as an array
get_field(name) - return the field object for given name
has_field(name) - return a bool if the field exists in the schema
cast_row(row, no_fail_fast=False) - return row cast against schema
save(target) - save schema to filesystem
Where the option no_fail_fast is given, it will collect all errors it encouters and an exceptions.MultipleInvalid will be raised (if there are errors).
Field
from tableschema import Field
# Init field
field = Field({'name': 'name', type': 'number'})
# Cast a value
field.cast_value('12345') # -> 12345Data values can be cast to native Python objects with a Field instance. Type instances can be initialized with field descriptors. This allows formats and constraints to be defined.
Casting a value will check the value is of the expected type, is in the correct format, and complies with any constraints imposed by a schema. E.g. a date value (in ISO 8601 format) can be cast with a DateType instance. Values that can’t be cast will raise an InvalidCastError exception.
Casting a value that doesn’t meet the constraints will raise a ConstraintError exception.
validate
Given a schema as JSON file, url to JSON file, or a Python dict, validate returns True for a valid Table Schema, or raises an exception, SchemaValidationError. It validates only schema, not data against schema!
import io
import json
from tableschema import validate
with io.open('schema_to_validate.json') as stream:
descriptor = json.load(stream)
try:
tableschema.validate(descriptor)
except tableschema.exceptions.SchemaValidationError as exception:
# handle errorIt may be useful to report multiple errors when validating a schema. This can be done with no_fail_fast flag set to True.
try:
tableschema.validate(descriptor, no_fail_fast=True)
except tableschema.exceptions.MultipleInvalid as exception:
for error in exception.errors:
# handle errorinfer
Given headers and data, infer will return a Table Schema as a Python dict based on the data values. Given the data file, data_to_infer.csv:
id,age,name 1,39,Paul 2,23,Jimmy 3,36,Jane 4,28,Judy
Call infer with headers and values from the datafile:
import io
import csv
from tableschema import infer
filepath = 'data_to_infer.csv'
with io.open(filepath) as stream:
headers = stream.readline().rstrip('\n').split(',')
values = csv.reader(stream)
schema = infer(headers, values)schema is now a schema dict:
{u'fields': [
{
u'description': u'',
u'format': u'default',
u'name': u'id',
u'title': u'',
u'type': u'integer'
},
{
u'description': u'',
u'format': u'default',
u'name': u'age',
u'title': u'',
u'type': u'integer'
},
{
u'description': u'',
u'format': u'default',
u'name': u'name',
u'title': u'',
u'type': u'string'
}]
}The number of rows used by infer can be limited with the row_limit argument.
CLI
It’s a provisional API excluded from SemVer. If you use it as a part of other program please pin concrete goodtables version to your requirements file.
Table Schema features a CLI called tableschema. This CLI exposes the infer and validate functions for command line use.
Example of validate usage:
$ tableschema validate path/to-schema.json
Example of infer usage:
$ tableschema infer path/to/data.csv
The response is a schema as JSON. The optional argument --encoding allows a character encoding to be specified for the data file. The default is utf-8.
Storage
The library includes interface declaration to implement tabular Storage:
An implementor should follow tableschema.Storage interface to write his own storage backend. This backend could be used with Table class. See plugins system below to know how to integrate custom storage plugin.
plugins
Table Schema has a plugin system. Any package with the name like tableschema_<name> could be imported as:
from tableschema.plugins import <name>If a plugin is not installed ImportError will be raised with a message describing how to install the plugin.
A list of officially supported plugins:
BigQuery Storage - https://github.com/frictionlessdata/tableschema-bigquery-py
Pandas Storage - https://github.com/frictionlessdata/tableschema-pandas-py
SQL Storage - https://github.com/frictionlessdata/tableschema-sql-py
API Reference
Snapshot
Table(source, schema=None, post_cast=None, backend=None, **options)
stream -> tabulator.Stream
schema -> Schema
name -> str
iter(keyed/extended=False) -> (generator) (keyed/extended)row[]
read(keyed/extended=False, limit=None) -> (keyed/extended)row[]
save(target, backend=None, **options)
Schema(descriptor)
descriptor -> dict
fields -> Field[]
headers -> str[]
primary_key -> str[]
foreign_keys -> str[]
get_field(name) -> Field
has_field(name) -> bool
cast_row(row, no_fail_fast=False) -> row
save(target)
Field(descriptor)
descriptor -> dict
name -> str
type -> str
format -> str
constraints -> dict
cast_value(value, constraints=True) -> value
test_value(value, constraints=True) -> bool
validate(descriptor, no_fail_fast=False) -> bool
infer(headers, values) -> descriptor
exceptions
~cli
---
Storage(**options)
buckets -> str[]
create(bucket, descriptor, force=False)
delete(bucket=None, ignore=False)
describe(bucket, descriptor=None) -> descriptor
iter(bucket) -> (generator) row[]
read(bucket) -> row[]
write(bucket, rows)
plugins
Detailed
Contributing
Please read the contribution guideline:
Thanks!
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