Function create

create function can create a new dataset on disk or in memory.

Parameters description for create():

Name	Type	Description	Default
datastore	string	A backend datastore, i.e., `'clingo'`, `'duckdb'`, or `'rdflib'`	REQUIRED
dataset	string	Name of the dataset to be created. Note that `':memory:'` is a reserved value for datasets that exist only in memory	REQUIRED
inputfile	string	A file to be loaded	REQUIRED
inputformat	string	Format of the file to be loaded	REQUIRED
isinputpath	bool	`True` if the inputfile is the file path, otherwise the inputfile is the content	REQUIRED
config	dict	A dictionary with configurations for certain backend store	see below

Description for the config parameter:

datastore: clingo

Name	Type	Description	Default
predicate	string	`'isfirstcol'` for using the first column as the predicate name; strings other than `'isfirstcol'` are used as the predicate name directly	`'isfirstcol'`
programname	string	Name of the program	`'base'`

Example 1: create a test ASP dataset on disk from a string

The .geistdata/clingo/test.pkl file is created and a Control object is returned.

import geist

lp_str = """
friends(a, b).
friends(a, c).
"""

# Create a Control object
conn = geist.create(datastore='clingo', dataset='test', inputfile=lp_str, inputformat="lp", isinputpath=False)

Example 2: create a test ASP dataset on disk from a file

The .geistdata/clingo/test.pkl file is created and a Control object is returned.

Here is the friends.lp file:

friends(a, b).
friends(a, c).

Code:

import geist

# Create a Control object
conn = geist.create(datastore='clingo', dataset='test', inputfile="friends.lp", inputformat="lp", isinputpath=True)

Example 3: create an ASP dataset in memory from a string

A Control object is returned.

import geist

lp_str = """
friends(a, b).
friends(a, c).
"""

# Create a Control object
conn = geist.create(datastore='clingo', dataset=':memory:', inputfile=lp_str, inputformat="lp", isinputpath=False)

datastore: duckdb

Name	Type	Description	Default
table	string	Name of the table to be created	`'df'`

Example 1: create a test SQL dataset on disk from a string

The .geistdata/duckdb/test.duckdb file is created and a DuckDBPyConnection object is returned.

import geist

csv_str = """
v1,v2,v3
1,2,3
4,5,6
7,8,9
"""

# Create a DuckPyConnection object
conn = geist.create(datastore='duckdb', dataset='test', inputfile=csv_str, inputformat="csv", isinputpath=False, config={"table": "df"})

Example 2: create a test SQL dataset on disk from a file

The .geistdata/duckdb/test.duckdb file is created and a DuckDBPyConnection object is returned.

Here is the test.csv file:

v1,v2,v3
1,2,3
4,5,6
7,8,9

Code:

import geist

# Create a DuckPyConnection object
conn = geist.create(datastore='duckdb', dataset='test', inputfile="test.csv", inputformat="csv", isinputpath=True, config={"table": "df"})

Example 3: create a SQL dataset in memory from a string

A DuckDBPyConnection object is returned.

import geist

csv_str = """
v1,v2,v3
1,2,3
4,5,6
7,8,9
"""

# Create a DuckPyConnection object
conn = geist.create(datastore='duckdb', dataset=':memory:', inputfile=csv_str, inputformat="csv", isinputpath=False, config={"table": "df"})

Example 4: create a SQL dataset in memory from a file

A DuckDBPyConnection object is returned.

Here is the test.csv file:

v1,v2,v3
1,2,3
4,5,6
7,8,9

Code:

import geist

# Create a DuckPyConnection object
conn = geist.create(datastore='duckdb', dataset=':memory:', inputfile="test.csv", inputformat="csv", isinputpath=True, config={"table": "df"})

datastore: rdflib

Name	Type	Description	Default
colnames	string	Column names of triples with the format of `[[subject1, predicate1, object1], [subject2, predicate2, object2], ...]`	REQUIRED when `inputformat='csv'`
infer	string	Inference to perform on update, i.e., `'none'`, `'rdfs'`, `'owl'`, or `'rdfs_owl'`	`'none'`

Example 1: create a test RDF dataset on disk from a string

The .geistdata/rdflib/test.pkl file is created and a GeistGraph object is returned.

import geist

csv_str = """
subject,predicate,object
<http://example.com/drewp>,<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>,<http://xmlns.com/foaf/0.1/Person>
<http://example.com/drewp>,<http://example.com/says>,"Hello World"
"""

# Create a GeistGraph object: a dictionary with 'rdf_graph' and 'infer' keys
conn = geist.create(datastore='rdflib', dataset='test', inputfile=csv_str, inputformat="csv", isinputpath=False, config={"colnames": "[['subject', 'predicate', 'object']]"})

Example 2: create a test RDF dataset on disk from a file

The .geistdata/rdflib/test.pkl file is created and a GeistGraph object is returned.

Here is the test.csv file:

subject,predicate,object
<http://example.com/drewp>,<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>,<http://xmlns.com/foaf/0.1/Person>
<http://example.com/drewp>,<http://example.com/says>,"Hello World"

Code:

import geist

# Create a GeistGraph object: a dictionary with 'rdf_graph' and 'infer' keys
conn = geist.create(datastore='rdflib', dataset='test', inputfile="test.csv", inputformat="csv", isinputpath=True, config={"colnames": "[['subject', 'predicate', 'object']]"})

Example 3: create a RDF dataset in memory from a string

A GeistGraph object is returned.

import geist

csv_str = """
subject,predicate,object
<http://example.com/drewp>,<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>,<http://xmlns.com/foaf/0.1/Person>
<http://example.com/drewp>,<http://example.com/says>,"Hello World"
"""

# Create a GeistGraph object: a dictionary with 'rdf_graph' and 'infer' keys
conn = geist.create(datastore='rdflib', dataset=':memory:', inputfile=csv_str, inputformat='csv', isinputpath=False, config={"colnames": "[['subject', 'predicate', 'object']]"})

Example 4: create a RDF dataset in memory from a file

A GeistGraph object is returned.

Here is the test.csv file:

subject,predicate,object
<http://example.com/drewp>,<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>,<http://xmlns.com/foaf/0.1/Person>
<http://example.com/drewp>,<http://example.com/says>,"Hello World"

Code:

import geist

# Create a GeistGraph object: a dictionary with 'rdf_graph' and 'infer' keys
conn = geist.create(datastore='rdflib', dataset=':memory:', inputfile='test.csv', inputformat='csv', isinputpath=True, config={"colnames": "[['subject', 'predicate', 'object']]"})