Load data

Rumo keeps the data in memory. Rumo does not write temporary files.

Each data item has a resource name. The rule program uses this name to read the data.

Load a DataFrame

rumo.read_dataframe(df)

The DataFrame can be a pandas DataFrame or a Polars DataFrame. Rumo converts the DataFrame to Turtle. Refer to DataFrame to RDF conversion.

Argument	Default	Description
`df`		The pandas DataFrame or Polars DataFrame.
`resource`	`"data.ttl"`	The resource name.
`predicate`	`None`	The predicate for the data. Refer to Connect the data to the rules.
`base_url`	`"http://example.org/"`	The prefix IRI.
`row_stem`	`"r"`	The start of the name of each row.

Note: For a pandas DataFrame, Rumo uses Polars to convert the data. If pyarrow is available, Rumo uses it. If pyarrow is not available, Rumo uses Python lists.

Load data from a string

rumo.read_data_str("""
@prefix : <http://example.org/> .
:a :name "Alice" .
:b :name "Bob" .
""", predicate="data")

Argument	Default	Description
`data`		The data as text.
`format`	`"turtle"`	The nemo import format.
`resource`	`"data.ttl"`	The resource name.
`predicate`	`None`	The predicate for the data.

Load data from a file

rumo.read_data_file("examples/sample.ttl", predicate="data")

Argument	Default	Description
`path`		The path of the file.
`format`	From the file extension	The nemo import format.
`resource`	The file name	The resource name.
`predicate`	`None`	The predicate for the data.

Rumo reads the file immediately.

If you do not set format, Rumo uses the file extension:

Extension	Format
`.nt`	`ntriples`
`.nq`	`nquads`
`.rdf`, `.xml`	`rdfxml`
`.csv`	`csv`
`.tsv`	`tsv`
`.trig`	`trig`
`.json`	`json`
Other extensions	`turtle`

Connect the data to the rules

There are two methods to connect the data to the rule program.

Method 1: Set a predicate

Set the predicate argument. Rumo then adds an @import statement to the rule program. For example, predicate="data" adds this statement:

@import data :- turtle { resource = "data.ttl" } .

For RDF data, the predicate has three columns: subject, property and value. Use it in the rules like this:

@prefix : <http://example.org/> .
named(?row, ?name) :- data(?row, :name, ?name) .

Use this method when you write the rules for Rumo.

Method 2: Use the resource name in the rules

Do not set the predicate argument. Set the resource argument to the name in the @import statement of the rules.

For example, examples/sample.rls has this statement:

@import r :- turtle { resource = "sample.ttl" } .

To run this file on a DataFrame, set resource="sample.ttl":

rumo = Rumo(rules_file="examples/sample.rls", data=df, resource="sample.ttl")

Rumo gives the DataFrame to the rules. Rumo does not read the file sample.ttl.

Use this method when you have rule files that already have @import statements.

Load more than one data item

You can load more than one data item. Give each data item a different resource name:

rumo.read_dataframe(people, resource="people.ttl", predicate="person")
rumo.read_dataframe(orders, resource="orders.ttl", predicate="order", row_stem="o")

Caution: If you load data with a resource name that already exists, the new data replaces the old data.

Caution: Two DataFrames with the same base_url and row_stem give the same row names (:r0, :r1, …). To keep the rows different, set a different row_stem for each DataFrame.

See the loaded data

The resources property gives the resource names:

print(rumo.resources)   # ['people.ttl', 'orders.ttl']

Remove the data

rumo.reset_data()

rumo manual