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datapackage

Utilities to work with Data Packages as defined on specs.frictionlessdata.io

1.15.4
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Maintainers: 5

datapackage-py

A library for working with Data Packages.

[Important Notice] We have released Frictionless Framework. This framework provides improved datapackage functionality extended to be a complete data solution. The change in not breaking for the existing software so no actions are required. Please read the Migration Guide from datapackage to Frictionless Framework.

Features

Package class for working with data packages
Resource class for working with data resources
Profile class for working with profiles
validate function for validating data package descriptors
infer function for inferring data package descriptors

Getting Started

Installation

The package use semantic versioning. It means that major versions could include breaking changes. It's highly recommended to specify datapackage version range in your setup/requirements file e.g. datapackage>=1.0,<2.0.

$ pip install datapackage

OSX 10.14+

If you receive an error about the cchardet package when installing datapackage on Mac OSX 10.14 (Mojave) or higher, follow these steps:

Make sure you have the latest x-code by running the following in terminal: xcode-select --install
Then go to https://developer.apple.com/download/more/ and download the command line tools. Note, this requires an Apple ID.
Then, in terminal, run open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg You can read more about these steps in this post.

Documentation

Introduction

Let's start with a simple example:

from datapackage import Package

package = Package('datapackage.json')
package.get_resource('resource').read()

Working with Package

A class for working with data packages. It provides various capabilities like loading local or remote data package, inferring a data package descriptor, saving a data package descriptor and many more.

Consider we have some local csv files in a data directory. Let's create a data package based on this data using a Package class:

data/cities.csv

city,location
london,"51.50,-0.11"
paris,"48.85,2.30"
rome,"41.89,12.51"

data/population.csv

city,year,population
london,2017,8780000
paris,2017,2240000
rome,2017,2860000

First we create a blank data package:

package = Package()

Now we're ready to infer a data package descriptor based on data files we have. Because we have two csv files we use glob pattern **/*.csv:

package.infer('**/*.csv')
package.descriptor
#{ profile: 'tabular-data-package',
#  resources:
#   [ { path: 'data/cities.csv',
#       profile: 'tabular-data-resource',
#       encoding: 'utf-8',
#       name: 'cities',
#       format: 'csv',
#       mediatype: 'text/csv',
#       schema: [Object] },
#     { path: 'data/population.csv',
#       profile: 'tabular-data-resource',
#       encoding: 'utf-8',
#       name: 'population',
#       format: 'csv',
#       mediatype: 'text/csv',
#       schema: [Object] } ] }

An infer method has found all our files and inspected it to extract useful metadata like profile, encoding, format, Table Schema etc. Let's tweak it a little bit:

package.descriptor['resources'][1]['schema']['fields'][1]['type'] = 'year'
package.commit()
package.valid # true

Because our resources are tabular we could read it as a tabular data:

package.get_resource('population').read(keyed=True)
#[ { city: 'london', year: 2017, population: 8780000 },
#  { city: 'paris', year: 2017, population: 2240000 },
#  { city: 'rome', year: 2017, population: 2860000 } ]

Let's save our descriptor on the disk as a zip-file:

package.save('datapackage.zip')

To continue the work with the data package we just load it again but this time using local datapackage.zip:

package = Package('datapackage.zip')
# Continue the work

It was onle basic introduction to the Package class. To learn more let's take a look on Package class API reference.

Working with Resource

A class for working with data resources. You can read or iterate tabular resources using the iter/read methods and all resource as bytes using row_iter/row_read methods.

Consider we have some local csv file. It could be inline data or remote link - all supported by Resource class (except local files for in-brower usage of course). But say it's data.csv for now:

city,location
london,"51.50,-0.11"
paris,"48.85,2.30"
rome,N/A

Let's create and read a resource. Because resource is tabular we could use resource.read method with a keyed option to get an array of keyed rows:

resource = Resource({path: 'data.csv'})
resource.tabular # true
resource.read(keyed=True)
# [
#   {city: 'london', location: '51.50,-0.11'},
#   {city: 'paris', location: '48.85,2.30'},
#   {city: 'rome', location: 'N/A'},
# ]
resource.headers
# ['city', 'location']
# (reading has to be started first)

As we could see our locations are just a strings. But it should be geopoints. Also Rome's location is not available but it's also just a N/A string instead of Python None. First we have to infer resource metadata:

resource.infer()
resource.descriptor
#{ path: 'data.csv',
#  profile: 'tabular-data-resource',
#  encoding: 'utf-8',
#  name: 'data',
#  format: 'csv',
#  mediatype: 'text/csv',
# schema: { fields: [ [Object], [Object] ], missingValues: [ '' ] } }
resource.read(keyed=True)
# Fails with a data validation error

Let's fix not available location. There is a missingValues property in Table Schema specification. As a first try we set missingValues to N/A in resource.descriptor.schema. Resource descriptor could be changed in-place but all changes should be commited by resource.commit():

resource.descriptor['schema']['missingValues'] = 'N/A'
resource.commit()
resource.valid # False
resource.errors
# [<ValidationError: "'N/A' is not of type 'array'">]

As a good citiziens we've decided to check out recource descriptor validity. And it's not valid! We should use an array for missingValues property. Also don't forget to have an empty string as a missing value:

resource.descriptor['schema']['missingValues'] = ['', 'N/A']
resource.commit()
resource.valid # true

All good. It looks like we're ready to read our data again:

resource.read(keyed=True)
# [
#   {city: 'london', location: [51.50,-0.11]},
#   {city: 'paris', location: [48.85,2.30]},
#   {city: 'rome', location: null},
# ]

Now we see that:

locations are arrays with numeric lattide and longitude
Rome's location is a native JavaScript null

And because there are no errors on data reading we could be sure that our data is valid againt our schema. Let's save our resource descriptor:

resource.save('dataresource.json')

Let's check newly-crated dataresource.json. It contains path to our data file, inferred metadata and our missingValues tweak:

{
    "path": "data.csv",
    "profile": "tabular-data-resource",
    "encoding": "utf-8",
    "name": "data",
    "format": "csv",
    "mediatype": "text/csv",
    "schema": {
        "fields": [
            {
                "name": "city",
                "type": "string",
                "format": "default"
            },
            {
                "name": "location",
                "type": "geopoint",
                "format": "default"
            }
        ],
        "missingValues": [
            "",
            "N/A"
        ]
    }
}

If we decide to improve it even more we could update the dataresource.json file and then open it again using local file name:

resource = Resource('dataresource.json')
# Continue the work

It was onle basic introduction to the Resource class. To learn more let's take a look on Resource class API reference.

Working with Group

A class representing a group of tabular resources. Groups can be used to read multiple resource as one or to export them, for example, to a database as one table. To define a group add the group: <name> field to corresponding resources. The group's metadata will be created from the "leading" resource's metadata (the first resource with the group name).

Consider we have a data package with two tables partitioned by a year and a shared schema stored separately:

cars-2017.csv

name,value
bmw,2017
tesla,2017
nissan,2017

cars-2018.csv

name,value
bmw,2018
tesla,2018
nissan,2018

cars.schema.json

{
    "fields": [
        {
            "name": "name",
            "type": "string"
        },
        {
            "name": "value",
            "type": "integer"
        }
    ]
}

datapackage.json

{
    "name": "datapackage",
    "resources": [
        {
            "group": "cars",
            "name": "cars-2017",
            "path": "cars-2017.csv",
            "profile": "tabular-data-resource",
            "schema": "cars.schema.json"
        },
        {
            "group": "cars",
            "name": "cars-2018",
            "path": "cars-2018.csv",
            "profile": "tabular-data-resource",
            "schema": "cars.schema.json"
        }
    ]
}

Let's read the resources separately:

package = Package('datapackage.json')
package.get_resource('cars-2017').read(keyed=True) == [
    {'name': 'bmw', 'value': 2017},
    {'name': 'tesla', 'value': 2017},
    {'name': 'nissan', 'value': 2017},
]
package.get_resource('cars-2018').read(keyed=True) == [
    {'name': 'bmw', 'value': 2018},
    {'name': 'tesla', 'value': 2018},
    {'name': 'nissan', 'value': 2018},
]

On the other hand, these resources defined with a group: cars field. It means we can treat them as a group:

package = Package('datapackage.json')
package.get_group('cars').read(keyed=True) == [
    {'name': 'bmw', 'value': 2017},
    {'name': 'tesla', 'value': 2017},
    {'name': 'nissan', 'value': 2017},
    {'name': 'bmw', 'value': 2018},
    {'name': 'tesla', 'value': 2018},
    {'name': 'nissan', 'value': 2018},
]

We can use this approach when we need to save the data package to a storage, for example, to a SQL database. There is the merge_groups flag to enable groupping behaviour:

package = Package('datapackage.json')
package.save(storage='sql', engine=engine)
# SQL tables:
# - cars-2017
# - cars-2018
package.save(storage='sql', engine=engine, merge_groups=True)
# SQL tables:
# - cars

Working with Profile

A component to represent JSON Schema profile from Profiles Registry:

profile = Profile('data-package')

profile.name # data-package
profile.jsonschema # JSON Schema contents

try:
   valid = profile.validate(descriptor)
except exceptions.ValidationError as exception:
   for error in exception.errors:
       # handle individual error

Working with Foreign Keys

The library supports foreign keys described in the Table Schema specification. It means if your data package descriptor use resources[].schema.foreignKeys property for some resources a data integrity will be checked on reading operations.

Consider we have a data package:

DESCRIPTOR = {
  'resources': [
    {
      'name': 'teams',
      'data': [
        ['id', 'name', 'city'],
        ['1', 'Arsenal', 'London'],
        ['2', 'Real', 'Madrid'],
        ['3', 'Bayern', 'Munich'],
      ],
      'schema': {
        'fields': [
          {'name': 'id', 'type': 'integer'},
          {'name': 'name', 'type': 'string'},
          {'name': 'city', 'type': 'string'},
        ],
        'foreignKeys': [
          {
            'fields': 'city',
            'reference': {'resource': 'cities', 'fields': 'name'},
          },
        ],
      },
    }, {
      'name': 'cities',
      'data': [
        ['name', 'country'],
        ['London', 'England'],
        ['Madrid', 'Spain'],
      ],
    },
  ],
}

Let's check relations for a teams resource:

from datapackage import Package

package = Package(DESCRIPTOR)
teams = package.get_resource('teams')
teams.check_relations()
# tableschema.exceptions.RelationError: Foreign key "['city']" violation in row "4"

As we could see there is a foreign key violation. That's because our lookup table cities doesn't have a city of Munich but we have a team from there. We need to fix it in cities resource:

package.descriptor['resources'][1]['data'].append(['Munich', 'Germany'])
package.commit()
teams = package.get_resource('teams')
teams.check_relations()
# True

Fixed! But not only a check operation is available. We could use relations argument for resource.iter/read methods to dereference a resource relations:

teams.read(keyed=True, relations=True)
#[{'id': 1, 'name': 'Arsenal', 'city': {'name': 'London', 'country': 'England}},
# {'id': 2, 'name': 'Real', 'city': {'name': 'Madrid', 'country': 'Spain}},
# {'id': 3, 'name': 'Bayern', 'city': {'name': 'Munich', 'country': 'Germany}}]

Instead of plain city name we've got a dictionary containing a city data. These resource.iter/read methods will fail with the same as resource.check_relations error if there is an integrity issue. But only if relations=True flag is passed.

Working with validate/infer

A standalone function to validate a data package descriptor:

from datapackage import validate, exceptions

try:
    valid = validate(descriptor)
except exceptions.ValidationError as exception:
   for error in exception.errors:
       # handle individual error

A standalone function to infer a data package descriptor.

descriptor = infer('**/*.csv')
#{ profile: 'tabular-data-resource',
#  resources:
#   [ { path: 'data/cities.csv',
#       profile: 'tabular-data-resource',
#       encoding: 'utf-8',
#       name: 'cities',
#       format: 'csv',
#       mediatype: 'text/csv',
#       schema: [Object] },
#     { path: 'data/population.csv',
#       profile: 'tabular-data-resource',
#       encoding: 'utf-8',
#       name: 'population',
#       format: 'csv',
#       mediatype: 'text/csv',
#       schema: [Object] } ] }

Frequently Asked Questions

Accessing data behind a proxy server?

Before the package = Package("https://xxx.json") call set these environment variables:

import os

os.environ["HTTP_PROXY"] = 'xxx'
os.environ["HTTPS_PROXY"] = 'xxx'

API Reference

`cli`

cli()

Command-line interface

Usage: datapackage [OPTIONS] COMMAND [ARGS]...

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  infer
  validate

`Package`

Package(self,
        descriptor=None,
        base_path=None,
        strict=False,
        unsafe=False,
        storage=None,
        schema=None,
        default_base_path=None,
        **options)

Package representation

Arguments

descriptor (str/dict): data package descriptor as local path, url or object
base_path (str): base path for all relative paths
strict (bool): strict flag to alter validation behavior. Setting it to True leads to throwing errors on any operation with invalid descriptor
unsafe (bool): if True unsafe paths will be allowed. For more inforamtion https://specs.frictionlessdata.io/data-resource/#data-location. Default to False
storage (str/tableschema.Storage): storage name like sql or storage instance
options (dict): storage options to use for storage creation

Raises

DataPackageException: raises error if something goes wrong

`package.base_path`

Package's base path

Returns

str/None: returns the data package base path

`package.descriptor`

Package's descriptor

Returns

dict: descriptor

`package.errors`

Validation errors

Always empty in strict mode.

Returns

Exception[]: validation errors

`package.profile`

Package's profile

Returns

Profile: an instance of Profile class

`package.resource_names`

Package's resource names

Returns

str[]: returns an array of resource names

`package.resources`

Package's resources

Returns

Resource[]: returns an array of Resource instances

`package.valid`

Validation status

Always true in strict mode.

Returns

bool: validation status

`package.get_resource`

package.get_resource(name)

Get data package resource by name.

Arguments

name (str): data resource name

Returns

Resource/None: returns Resource instances or null if not found

`package.add_resource`

package.add_resource(descriptor)

Add new resource to data package.

The data package descriptor will be validated with newly added resource descriptor.

Arguments

descriptor (dict): data resource descriptor

Raises

DataPackageException: raises error if something goes wrong

Returns

Resource/None: returns added Resource instance or null if not added

`package.remove_resource`

package.remove_resource(name)

Remove data package resource by name.

The data package descriptor will be validated after resource descriptor removal.

Arguments

name (str): data resource name

Raises

DataPackageException: raises error if something goes wrong

Returns

Resource/None: returns removed Resource instances or null if not found

`package.get_group`

package.get_group(name)

Returns a group of tabular resources by name.

For more information about groups see Group.

Arguments

name (str): name of a group of resources

Raises

DataPackageException: raises error if something goes wrong

Returns

Group/None: returns a Group instance or null if not found

`package.infer`

package.infer(pattern=False)

Infer a data package metadata.

Argument pattern works only for local files

If pattern is not provided only existent resources will be inferred (added metadata like encoding, profile etc). If pattern is provided new resoures with file names mathing the pattern will be added and inferred. It commits changes to data package instance.

Arguments

pattern (str): glob pattern for new resources

Returns

dict: returns data package descriptor

`package.commit`

package.commit(strict=None)

Update data package instance if there are in-place changes in the descriptor.

Example

package = Package({
    'name': 'package',
    'resources': [{'name': 'resource', 'data': ['data']}]
})

package.name # package
package.descriptor['name'] = 'renamed-package'
package.name # package
package.commit()
package.name # renamed-package

Arguments

strict (bool): alter strict mode for further work

Raises

DataPackageException: raises error if something goes wrong

Returns

bool: returns true on success and false if not modified

`package.save`

package.save(target=None,
             storage=None,
             merge_groups=False,
             to_base_path=False,
             **options)

Saves this data package

It saves it to storage if storage argument is passed or saves this data package's descriptor to json file if target arguments ends with .json or saves this data package to zip file otherwise.

Example

It creates a zip file into file_or_path with the contents of this Data Package and its resources. Every resource which content lives in the local filesystem will be copied to the zip file. Consider the following Data Package descriptor:

{
    "name": "gdp",
    "resources": [
        {"name": "local", "format": "CSV", "path": "data.csv"},
        {"name": "inline", "data": [4, 8, 15, 16, 23, 42]},
        {"name": "remote", "url": "http://someplace.com/data.csv"}
    ]
}

The final structure of the zip file will be:

./datapackage.json
./data/local.csv

With the contents of datapackage.json being the same as returned datapackage.descriptor. The resources' file names are generated based on their name and format fields if they exist. If the resource has no name, it'll be used resource-X, where X is the index of the resource in the resources list (starting at zero). If the resource has format, it'll be lowercased and appended to the name, becoming "name.format".

Arguments

target (string/filelike): the file path or a file-like object where the contents of this Data Package will be saved into.
storage (str/tableschema.Storage): storage name like sql or storage instance
merge_groups (bool): save all the group's tabular resoruces into one bucket if a storage is provided (for example into one SQL table). Read more about Group.
to_base_path (bool): save the package to the package's base path using the "<base_path>/<target>" route
options (dict): storage options to use for storage creation

Raises

DataPackageException: raises if there was some error writing the package

Returns

bool/Storage: on success return true or a Storage instance

`Resource`

Resource(self,
         descriptor={},
         base_path=None,
         strict=False,
         unsafe=False,
         storage=None,
         package=None,
         **options)

Resource represenation

Arguments

descriptor (str/dict): data resource descriptor as local path, url or object
base_path (str): base path for all relative paths
strict (bool): strict flag to alter validation behavior. Setting it to true leads to throwing errors on any operation with invalid descriptor
unsafe (bool): if True unsafe paths will be allowed. For more inforamtion https://specs.frictionlessdata.io/data-resource/#data-location. Default to False
storage (str/tableschema.Storage): storage name like sql or storage instance
options (dict): storage options to use for storage creation

Raises

DataPackageException: raises error if something goes wrong

`resource.data`

Return resource data

`resource.descriptor`

Package's descriptor

Returns

dict: descriptor

`resource.errors`

Validation errors

Always empty in strict mode.

Returns

Exception[]: validation errors

`resource.group`

Group name

Returns

str: group name

`resource.headers`

Resource's headers

Only for tabular resources (reading has to be started first or it's None)

Returns

str[]/None: returns data source headers

`resource.inline`

Whether resource inline

Returns

bool: returns true if resource is inline

`resource.local`

Whether resource local

Returns

bool: returns true if resource is local

`resource.multipart`

Whether resource multipart

Returns

bool: returns true if resource is multipart

`resource.name`

Resource name

Returns

str: name

`resource.package`

Package instance if the resource belongs to some package

Returns

Package/None: a package instance if available

`resource.profile`

Resource's profile

Returns

Profile: an instance of Profile class

`resource.remote`

Whether resource remote

Returns

bool: returns true if resource is remote

`resource.schema`

Resource's schema

Only for tabular resources

For tabular resources it returns Schema instance to interact with data schema. Read API documentation - tableschema.Schema.

Returns

tableschema.Schema: schema

`resource.source`

Resource's source

Combination of resource.source and resource.inline/local/remote/multipart provides predictable interface to work with resource data.

Returns

list/str: returns data or path property

`resource.table`

Return resource table

`resource.tabular`

Whether resource tabular

Returns

bool: returns true if resource is tabular

`resource.valid`

Validation status

Always true in strict mode.

Returns

bool: validation status

`resource.iter`

resource.iter(integrity=False, relations=False, **options)

Iterates through the resource data and emits rows cast based on table schema.

Only for tabular resources

Arguments

keyed (bool):
    yield keyed rows in a form of `{header1: value1, header2: value2}`
    (default is false; the form of rows is `[value1, value2]`)

extended (bool):
    yield extended rows in a for of `[rowNumber, [header1, header2], [value1, value2]]`
    (default is false; the form of rows is `[value1, value2]`)

cast (bool):
    disable data casting if false
    (default is true)

integrity (bool):
    if true actual size in BYTES and SHA256 hash of the file
    will be checked against `descriptor.bytes` and `descriptor.hash`
    (other hashing algorithms are not supported and will be skipped silently)

relations (bool):
    if true foreign key fields will be checked and resolved to its references

foreign_keys_values (dict):
    three-level dictionary of foreign key references optimized
    to speed up validation process in a form of
    `{resource1: {(fk_field1, fk_field2): {(value1, value2): {one_keyedrow}, ... }}}`.
    If not provided but relations is true, it will be created
    before the validation process by *index_foreign_keys_values* method

exc_handler (func):
    optional custom exception handler callable.
    Can be used to defer raising errors (i.e. "fail late"), e.g.
    for data validation purposes. Must support the signature below

Custom exception handler

def exc_handler(exc, row_number=None, row_data=None, error_data=None):
    '''Custom exception handler (example)

    # Arguments:
        exc(Exception):
            Deferred exception instance
        row_number(int):
            Data row number that triggers exception exc
        row_data(OrderedDict):
            Invalid data row source data
        error_data(OrderedDict):
            Data row source data field subset responsible for the error, if
            applicable (e.g. invalid primary or foreign key fields). May be
            identical to row_data.
    '''
    # ...

Raises

DataPackageException: base class of any error
CastError: data cast error
IntegrityError: integrity checking error
UniqueKeyError: unique key constraint violation
UnresolvedFKError: unresolved foreign key reference error

Returns

Iterator[list]: yields rows

`resource.read`

resource.read(integrity=False,
              relations=False,
              foreign_keys_values=False,
              **options)

Read the whole resource and return as array of rows

Only for tabular resources It has the same API as resource.iter except for

Arguments

limit (int): limit count of rows to read and return

Returns

list[]: returns rows

`resource.check_integrity`

resource.check_integrity()

Checks resource integrity

Only for tabular resources

It checks size in BYTES and SHA256 hash of the file against descriptor.bytes and descriptor.hash (other hashing algorithms are not supported and will be skipped silently).

Raises

exceptions.IntegrityError: raises if there are integrity issues

Returns

bool: returns True if no issues

`resource.check_relations`

resource.check_relations(foreign_keys_values=False)

Check relations

Only for tabular resources

It checks foreign keys and raises an exception if there are integrity issues.

Raises

exceptions.RelationError: raises if there are relation issues

Returns

bool: returns True if no issues

`resource.drop_relations`

resource.drop_relations()

Drop relations

Only for tabular resources

Remove relations data from memory

Returns

bool: returns True

`resource.raw_iter`

resource.raw_iter(stream=False)

Iterate over data chunks as bytes.

If stream is true File-like object will be returned.

Arguments

stream (bool): File-like object will be returned

Returns

bytes[]/filelike: returns bytes[]/filelike

`resource.raw_read`

resource.raw_read()

Returns resource data as bytes.

Returns

bytes: returns resource data in bytes

`resource.infer`

resource.infer(**options)

Infer resource metadata

Like name, format, mediatype, encoding, schema and profile. It commits this changes into resource instance.

Arguments

options: options will be passed to tableschema.infer call, for more control on results (e.g. for setting limit, confidence etc.).

Returns

dict: returns resource descriptor

`resource.commit`

resource.commit(strict=None)

Update resource instance if there are in-place changes in the descriptor.

Arguments

strict (bool): alter strict mode for further work

Raises

DataPackageException: raises error if something goes wrong

Returns

bool: returns true on success and false if not modified

`resource.save`

resource.save(target, storage=None, to_base_path=False, **options)

Saves this resource

Into storage if storage argument is passed or saves this resource's descriptor to json file otherwise.

Arguments

target (str): path where to save a resource
storage (str/tableschema.Storage): storage name like sql or storage instance
to_base_path (bool): save the resource to the resource's base path using the "<base_path>/<target>" route
options (dict): storage options to use for storage creation

Raises

DataPackageException: raises error if something goes wrong

Returns

bool: returns true on success Building index... Started generating documentation...

`Group`

Group(self, resources)

Group representation

Arguments

Resource[]: list of TABULAR resources

`group.headers`

Group's headers

Returns

str[]/None: returns headers

`group.name`

Group name

Returns

str: name

`group.schema`

Resource's schema

Returns

tableschema.Schema: schema

`group.iter`

group.iter(**options)

Iterates through the group data and emits rows cast based on table schema.

It concatenates all the resources and has the same API as resource.iter

`group.read`

group.read(limit=None, **options)

Read the whole group and return as array of rows

It concatenates all the resources and has the same API as resource.read

`group.check_relations`

group.check_relations()

Check group's relations

The same as resource.check_relations but without the optional argument foreign_keys_values. This method will test foreignKeys of the whole group at once otpimizing the process by creating the foreign_key_values hashmap only once before testing the set of resources.

`Profile`

Profile(self, profile)

Profile representation

Arguments

profile (str): profile name in registry or URL to JSON Schema

Raises

DataPackageException: raises error if something goes wrong

`profile.jsonschema`

JSONSchema content

Returns

dict: returns profile's JSON Schema contents

`profile.name`

Profile name

Returns

str/None: name if available

`profile.validate`

profile.validate(descriptor)

Validate a data package descriptor against the profile.

Arguments

descriptor (dict): retrieved and dereferenced data package descriptor

Raises

ValidationError: raises if not valid Returns

bool: returns True if valid

`validate`

validate(descriptor)

Validate a data package descriptor.

Arguments

descriptor (str/dict): package descriptor (one of): - local path - remote url - object

Raises

ValidationError: raises on invalid

Returns

bool: returns true on valid

`infer`

infer(pattern, base_path=None)

Infer a data package descriptor.

Argument pattern works only for local files

Arguments

pattern (str): glob file pattern

Returns

dict: returns data package descriptor

`DataPackageException`

DataPackageException(self, message, errors=[])

Base class for all DataPackage/TableSchema exceptions.

If there are multiple errors, they can be read from the exception object:

try:
    # lib action
except DataPackageException as exception:
    if exception.multiple:
        for error in exception.errors:
            # handle error

`datapackageexception.errors`

List of nested errors

Returns

DataPackageException[]: list of nested errors

`datapackageexception.multiple`

Whether it's a nested exception

Returns

bool: whether it's a nested exception

`TableSchemaException`

TableSchemaException(self, message, errors=[])

Base class for all TableSchema exceptions.

`LoadError`

LoadError(self, message, errors=[])

All loading errors.

`CastError`

CastError(self, message, errors=[])

All value cast errors.

`IntegrityError`

IntegrityError(self, message, errors=[])

All integrity errors.

`RelationError`

RelationError(self, message, errors=[])

All relations errors.

`StorageError`

StorageError(self, message, errors=[])

All storage errors.

Contributing

The project follows the Open Knowledge International coding standards.

Recommended way to get started is to create and activate a project virtual environment. To install package and development dependencies into active environment:

$ make install

To run tests with linting and coverage:

$ make test

Changelog

Here described only breaking and the most important changes. The full changelog and documentation for all released versions could be found in nicely formatted commit history.

v1.15

WARNING: it can be breaking for some setups, please read the discussions below

Fixed header management according to the specs:

v1.14

Add experimental options for pick/skiping fileds/rows

v1.13

Add unsafe option to Package and Resource (#262)

v1.12

Use chardet for encoding deteciton by default. For cchardet: pip install datapackage[cchardet]

v1.11

resource/package.save now accept a to_base_path argument (#254)
package.save now returns a Storage instance if available

v1.10

Added an ability to check tabular resource's integrity

v1.9

Added resource.package property

v1.8

Added support for groups of resources

v1.7

Added support for compression of resources

v1.6

Added support for custom request session

v1.5

Updated behaviour:

Added support for Python 3.7

v1.4

New API added:

added skip_rows support to the resource descriptor

v1.3

New API added:

property package.base_path is now publicly available

v1.2

Updated behaviour:

CLI command $ datapackage infer now outputs only a JSON-formatted data package descriptor.

v1.1

New API added:

Added an integration between Package/Resource and the tableschema.Storage - https://github.com/frictionlessdata/tableschema-py#storage. It allows to load and save data package from/to different storages like SQL/BigQuery/etc.

Keywords

FAQs

What is datapackage?

Is datapackage well maintained?

Did you know?

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Install

datapackage

datapackage-py

Features

Contents

Getting Started

Installation

OSX 10.14+

Documentation

Introduction

Working with Package

Working with Resource

Working with Group

Working with Profile

Working with Foreign Keys

Working with validate/infer

Frequently Asked Questions

Accessing data behind a proxy server?

API Reference

cli

Package

package.base_path

package.descriptor

package.errors

package.profile

package.resource_names

package.resources

package.valid

package.get_resource

package.add_resource

package.remove_resource

package.get_group

package.infer

package.commit

package.save

Resource

resource.data

resource.descriptor

resource.errors

resource.group

resource.headers

resource.inline

resource.local

resource.multipart

resource.name

resource.package

resource.profile

resource.remote

resource.schema

resource.source

resource.table

resource.tabular

resource.valid

resource.iter

resource.read

resource.check_integrity

resource.check_relations

resource.drop_relations

resource.raw_iter

resource.raw_read

resource.infer

resource.commit

resource.save

Group

group.headers

group.name

group.schema

group.iter

group.read

group.check_relations

Profile

profile.jsonschema

profile.name

profile.validate

validate

infer

DataPackageException

datapackageexception.errors

datapackageexception.multiple

TableSchemaException

LoadError

`cli`

`Package`

`package.base_path`

`package.descriptor`

`package.errors`

`package.profile`

`package.resource_names`

`package.resources`

`package.valid`

`package.get_resource`

`package.add_resource`

`package.remove_resource`

`package.get_group`

`package.infer`

`package.commit`

`package.save`

`Resource`

`resource.data`

`resource.descriptor`

`resource.errors`

`resource.group`

`resource.headers`

`resource.inline`

`resource.local`

`resource.multipart`

`resource.name`

`resource.package`

`resource.profile`

`resource.remote`

`resource.schema`

`resource.source`

`resource.table`

`resource.tabular`

`resource.valid`

`resource.iter`

`resource.read`

`resource.check_integrity`

`resource.check_relations`

`resource.drop_relations`

`resource.raw_iter`

`resource.raw_read`

`resource.infer`

`resource.commit`

`resource.save`

`Group`

`group.headers`

`group.name`

`group.schema`

`group.iter`

`group.read`

`group.check_relations`

`Profile`

`profile.jsonschema`

`profile.name`

`profile.validate`

`validate`

`infer`

`DataPackageException`

`datapackageexception.errors`

`datapackageexception.multiple`

`TableSchemaException`

`LoadError`

`CastError`

`IntegrityError`

`RelationError`

`StorageError`