Jul 5, 2023 · Apr 19, 2023 · Apr 19, 2023 · Jun 28, 2023 · Jun 28, 2023 · Jul 5, 2023
diff --git a/README.md b/README.md
 ```
 Similar methods exist for BinaryOp, Monoid, and Semiring.

 ## Relation to other network analysis libraries
 Python-graphblas aims to provide an efficient and consistent expression
 of graph operations using linear algebra. This allows the development of
 high-performance implementations of existing and new graph algorithms
 (also see [`graphblas-algorithms`](https://github.com/python-graphblas/graphblas-algorithms)).

 While end-to-end analysis can be done using `python-graphblas`, users
 might find that other libraries in the Python ecosystem provide a more
 convenient high-level interface for data pre-processing and transformation
 (e.g. `pandas`, `scipy.sparse`), visualization (e.g. `networkx`, `igraph`),
 interactive exploration and analysis (e.g. `networkx`, `igraph`) or for
 algorithms that are not (yet) implemented in `graphblas-algorithms` (e.g.
 `networkx`, `igraph`, `scipy.sparse.csgraph`). To facilitate communication with
 other libraries, `graphblas.io` contains multiple connectors, see the
 following section.

 ## Import/Export connectors to the Python ecosystem
 `graphblas.io` contains functions for converting to and from:
 ```python
Original file line number	Diff line number	Diff line change
Expand Up		@@ -204,6 +204,22 @@ w # indexes=[0, 1, 3], values=[1, 3, 3]
		```
		Similar methods exist for BinaryOp, Monoid, and Semiring.

		## Relation to other network analysis libraries
		Python-graphblas aims to provide an efficient and consistent expression
		of graph operations using linear algebra. This allows the development of
		high-performance implementations of existing and new graph algorithms
		(also see [`graphblas-algorithms`](https://github.com/python-graphblas/graphblas-algorithms)).

		While end-to-end analysis can be done using `python-graphblas`, users
		might find that other libraries in the Python ecosystem provide a more
		convenient high-level interface for data pre-processing and transformation
		(e.g. `pandas`, `scipy.sparse`), visualization (e.g. `networkx`, `igraph`),
		interactive exploration and analysis (e.g. `networkx`, `igraph`) or for
		algorithms that are not (yet) implemented in `graphblas-algorithms` (e.g.
		`networkx`, `igraph`, `scipy.sparse.csgraph`). To facilitate communication with
		other libraries, `graphblas.io` contains multiple connectors, see the
		following section.

		## Import/Export connectors to the Python ecosystem
		`graphblas.io` contains functions for converting to and from:
		```python
Expand Down