aito.schema.AitoTableSchema

class aito.schema.AitoTableSchema(columns: Dict[str, AitoColumnTypeSchema])

Bases: AitoSchema

Aito Table schema contains the columns and their schema

Can be thought of as a dict-like container for AitoColumnTypeSchema objects

Infer AitoTableSchema from a Pandas DataFrame

>>> import pandas as pd
>>> df = pd.DataFrame(data={'id': [1, 2], 'name': ['Neil', 'Buzz']})
>>> table_schema = AitoTableSchema.infer_from_pandas_data_frame(df)
>>> print(table_schema.to_json_string(indent=2, sort_keys=True))
{
  "columns": {
    "id": {
      "nullable": false,
      "type": "Int"
    },
    "name": {
      "nullable": false,
      "type": "String"
    }
  },
  "type": "table"
}

>>> print(table_schema['name'])
{"type": "String", "nullable": false}

change the property of a column

>>> table_schema['name'].nullable = True
>>> print(table_schema['name'])
{"type": "String", "nullable": true}

add a column to the table schema

>>> table_schema['description'] = AitoColumnTypeSchema(AitoTextType(), nullable=True)
>>> print(table_schema.to_json_string(indent=2, sort_keys=True))
{
  "columns": {
    "description": {
      "nullable": true,
      "type": "Text"
    },
    "id": {
      "nullable": false,
      "type": "Int"
    },
    "name": {
      "nullable": true,
      "type": "String"
    }
  },
  "type": "table"
}

delete a column in the table schema

>>> del table_schema['description']
>>> table_schema.columns
['id', 'name']

check if a column exist in the table schema

>>> 'id' in table_schema
True

iterate over the table schema >>> for col in table_schema: … table_schema[col].nullable = False

Parameters:: columns (Dict[str, AitoColumnTypeSchema]) – a dictionary of the table’s columns’ name and schema

Methods

`from_deserialized_object`(obj)	create a class object from a JSON deserialized object
`from_json_string`(json_string, **kwargs)	create an class object from a JSON string
`has_column`(column_name)	check if the table has the specified column
`infer_from_pandas_data_frame`(df[, ...])	Infer a TableSchema from a Pandas DataFrame
`json_schema`()	the JSON schema of the class
`json_schema_validate`(obj)	Validate an object with the class json_schema Returns the object if validation success, else raise `JsonValidationError`
`json_schema_validate_with_schema`(obj, schema)	Validate an object with the given schema
`to_json_serializable`()	convert the object to an object that can be serialized to a JSON formatted string
`to_json_string`(**kwargs)	convert the object to a JSON string

Attributes

`column_link_pattern`
`column_name_pattern`
`columns`	list of the table's columns name
`columns_schemas`	a dictionary contains the names of the table columns and its corresponding schemas
`comparison_properties`	properties of the schema object that will be used for comparison operation
`links`	a dictionary contains the names of the table columns and its corresponding link
`table_name_pattern`
`type`	the type of the schema component
`uuid_pattern`

property columns: List[str]

list of the table’s columns name

Return type:: List[str]

property columns_schemas: Dict[str, AitoColumnTypeSchema]

a dictionary contains the names of the table columns and its corresponding schemas

Return type:: Dict[str, AitoColumnTypeSchema]

property comparison_properties: Iterable[str]

properties of the schema object that will be used for comparison operation

Return type:: Iterable[str]

classmethod from_deserialized_object(obj): create a class object from a JSON deserialized object

classmethod from_json_string(json_string: str, **kwargs)

create an class object from a JSON string

Parameters:

json_string (str) – the JSON string
kwargs – the keyword arguments for json.loads method

has_column(column_name: str) → bool

check if the table has the specified column

Parameters:: column_name (str) – the name of the column
Returns:: true if the table has the specified column
Return type:: bool

classmethod infer_from_pandas_data_frame(df: pandas.DataFrame, max_sample_size: int = 100000) → AitoTableSchema

Infer a TableSchema from a Pandas DataFrame

Parameters:

df (pd.DataFrame) – input Pandas DataFrame
max_sample_size (int, optional) – maximum number of rows that will be used for inference, defaults to 100000

Raises:

Exception – an error occurred during column type inference

Returns:

inferred table schema

Return type:

Dict

classmethod json_schema()

the JSON schema of the class

Return type:: Dict

classmethod json_schema_validate(obj: Any)

Validate an object with the class json_schema Returns the object if validation success, else raise JsonValidationError

Parameters:: obj (Any) – the object to be validated
Returns:: the object if validation succeed
Return type:: Any

json_schema_validate_with_schema(obj: Any, schema: Dict)

Validate an object with the given schema

Parameters:

obj (Any) – the object to be validated
schema (Dict) – the schema to be validate against

Returns:

the object if validation succeed

Return type:

Any

property links: Dict[str, AitoColumnLinkSchema] | None

a dictionary contains the names of the table columns and its corresponding link

Return type:: Dict[str, AitoColumnLinkSchema]

to_json_serializable(): convert the object to an object that can be serialized to a JSON formatted string

to_json_string(**kwargs)

convert the object to a JSON string

Parameters:: kwargs – the keyword arguments for json.dumps method
Return type:: str

property type

the type of the schema component

Return type:: str