Create

create feature can create a new dataset on disk or in memory. For the disk option, a .duckdb or a .pkl file will be created under the .geistdata/duckdb or the .geistdata/rdflib folder with the same name of this dataset. For the memory option, a DuckDBPyConnection object or a GeistGraph object (a dictionary with rdf_graph and infer keys) will be returned.

You can write a Geist template with the create tag. You can also use CLI or Python API step by step as follows:

The create command has two subcommands, both of which create a new dataset on disk. The dataset name :memory: is a reserved value for datasets that exist only in memory and is not allowed in the CLI.

Usage: geist create [OPTIONS] COMMAND [ARGS]...

Create a new dataset

Options:
--help  Show this message and exit.

Commands:
duckdb  Create a new SQL dataset using DuckDB
rdflib  Create a new RDF dataset using RDFLib
geist create duckdb [OPTIONS] 
Usage: geist create duckdb [OPTIONS]

Create a new SQL dataset using DuckDB

Options:
-d, --dataset TEXT              Name of SQL dataset to create (default "kb")
-ifile, --inputfile FILENAME    Path of the file to be loaded as a Pandas
                                DataFrame  [required]
-iformat, --inputformat [csv|json]
                                Format of the file to be loaded as a Pandas
                                DataFrame (default csv)
-t, --table TEXT                Name of the table to be created (default
                                "df")
--help                          Show this message and exit.
Example 1: create a test SQL dataset from stdin 
geist create duckdb --dataset test --inputformat csv --table df << __END_INPUT__
v1,v2,v3
1,2,3
4,5,6
7,8,9
__END_INPUT__
Example 2: create a test dataset from a file

Here is the test.csv file:

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

Code: 

geist create duckdb --dataset test --inputfile test.csv --inputformat csv --table df

geist create rdflib [OPTIONS] 
Usage: geist create rdflib [OPTIONS]

Create a new RDF dataset

Options:
-d, --dataset TEXT              Name of RDF dataset to create (default "kb")
-ifile, --inputfile FILENAME    Path of the file to be loaded as triples
                                [required]
-iformat, --inputformat [xml|n3|turtle|nt|pretty-xml|trix|trig|nquads|json-ld|hext|csv]
                                Format of the file to be loaded as triples
                                (default json-ld)
--colnames TEXT                 Column names of triples with the format of
                                [[subject1, predicate1, object1], [subject2,
                                predicate2, object2], ...] when the input
                                format is csv
--infer [none|rdfs|owl|rdfs_owl]
                                Inference to perform on update [none, rdfs,
                                owl, rdfs_owl] (default "none")
--help                          Show this message and exit.
Example 1: create a test RDF dataset from stdin 
geist create rdflib --dataset test --inputformat nt --infer none << __END_INPUT__

<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" .

__END_INPUT__
Example 2: create a test dataset from a file

Here is the test.nt file:

<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: 
geist create rdflib --dataset test --inputfile test.nt --inputformat nt --infer none

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., 'rdflib' or 'duckdb' 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: 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']]"})

create method of the Connection class creates a new dataset on disk or in memory. It is very similar to the create() function. The only difference is that the datastore and the dataset parameters do not need to be passed as they have already been specified while initialzing the Connection class.

Parameters description for create method of the Connection class:

Name Type Description Default
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: duckdb
Key Type Description Default
table string Name of the table to be created 'df'
Example 1: create a test SQL dataset from a string

The .geistdata/duckdb/test.duckdb file is created and a Connection instance is returned.

import geist

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

# Create a Connection instance
connection = geist.Connection(datastore='duckdb', dataset='test')
connection.create(inputfile=csv_str, inputformat="csv", isinputpath=False, config={"table": "df"})
Example 2: create a test SQL dataset from a file

The .geistdata/duckdb/test.duckdb file is created and a Connection instance 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 Connection instance
connection = geist.Connection(datastore='duckdb', dataset='test')
connection.create(inputfile="test.csv", inputformat="csv", isinputpath=True, config={"table": "df"})

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

A Connection instance is returned.

import geist

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

# Create a Connection instance
connection = geist.Connection(datastore='duckdb', dataset=':memory:')
connection.create(inputfile=csv_str, inputformat="csv", isinputpath=False, config={"table": "df"})
Example 4: create a SQL dataset in memory from a file

A Connection instance 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 Connection instance
connection = geist.Connection(datastore='duckdb', dataset=':memory:')
connection.create(inputfile="test.csv", inputformat="csv", isinputpath=True, config={"table": "df"})

datastore: rdflib
Key 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 from a string

The .geistdata/rdflib/test.pkl file is created and a Connection instance 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 Connection instance
connection = geist.Connection(datastore='rdflib', dataset='test')
connection.create(inputfile=csv_str, inputformat="csv", isinputpath=False, config={"colnames": "[['subject', 'predicate', 'object']]"})
Example 2: create a test RDF dataset from a file

The .geistdata/rdflib/test.pkl file is created and a Connection instance 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 Connection instance
connection = geist.Connection(datastore='rdflib', dataset='test')
connection.create(inputfile="test.csv", inputformat="csv", isinputpath=True, config={"colnames": "[['subject', 'predicate', 'object']]"})

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

A Connection instance 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 Connection instance
connection = geist.Connection(datastore='rdflib', dataset=':memory:')
connection.create(inputfile=csv_str, inputformat="csv", isinputpath=False, config={"colnames": "[['subject', 'predicate', 'object']]"})
Example 4: create a RDF dataset in memory from a file

A Connection instance 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 Connection instance
connection = geist.Connection(datastore='rdflib', dataset=':memory:')
connection.create(inputfile="test.csv", inputformat="csv", isinputpath=True, config={"colnames": "[['subject', 'predicate', 'object']]"})