-Additionally, this project maintains compatibility with the
-[`numpy.ndarray`][] interface rather than the
-[`numpy.matrix`][] interface used in
-[`scipy.sparse`][]
-
-These differences make this project useful in certain situations where
-scipy.sparse matrices are not well suited, but it should not be
-considered a full replacement. The data structures in pydata/sparse
-complement and can be used in conjunction with the fast linear algebra
-routines inside [`scipy.sparse`][]. A format conversion or copy may be
-required.
-
-## Motivation
-
-Sparse arrays, or arrays that are mostly empty or filled with zeros, are
-common in many scientific applications. To save space we often avoid
-storing these arrays in traditional dense formats, and instead choose
-different data structures. Our choice of data structure can
-significantly affect our storage and computational costs when working
-with these arrays.
-
-## Design
-
-The main data structure in this library follows the [Coordinate List
-(COO)](https://en.wikipedia.org/wiki/Sparse_matrix#Coordinate_list_(COO))
-layout for sparse matrices, but extends it to multiple dimensions.
-
-The COO layout, which stores the row index, column index, and value of
-every element:
-
-
-| row | col | data |
-|-----|-----|------|
-| 0 | 0 | 10 |
-| 0 | 2 | 13 |
-| 1 | 3 | 9 |
-| 3 | 8 | 21 |
-
-It is straightforward to extend the COO layout to an arbitrary number of
-dimensions:
-
+ {width=10%, align=left}
+
Introduction
+ { .card }
-| dim1 | dim2 | dim3 | \... | data |
-|------|------|------|------|------|
-| 0 | 0 | 0 | . | 10 |
-| 0 | 0 | 3 | . | 13 |
-| 0 | 2 | 2 | . | 9 |
-| 3 | 1 | 4 | . | 21 |
+ {width=10%, align=left}
+
Install
+ { .card }
-This makes it easy to *store* a multidimensional sparse array, but we
-still need to reimplement all of the array operations like transpose,
-reshape, slicing, tensordot, reductions, etc., which can be challenging
-in general.
+ {width=10%, align=left}
+
Tutorials
+ { .card }
-This library also includes several other data structures. Similar to
-COO, the [Dictionary of Keys
-(DOK)](https://en.wikipedia.org/wiki/Sparse_matrix#Dictionary_of_keys_(DOK))
-format for sparse matrices generalizes well to an arbitrary number of
-dimensions. DOK is well-suited for writing and mutating. Most other
-operations are not supported for DOK. A common workflow may involve
-writing an array with DOK and then converting to another format for
-other operations.
+ {width=10%, align=left}
+
How-to guides
+ { .card }
-The [Compressed Sparse Row/Column
-(CSR/CSC)](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_column_(CSC_or_CCS))
-formats are widely used in scientific computing are now supported by
-pydata/sparse. The CSR/CSC formats excel at compression and mathematical
-operations. While these formats are restricted to two dimensions,
-pydata/sparse supports the GCXS sparse array format, based on [GCRS/GCCS
-from](https://ieeexplore.ieee.org/abstract/document/7237032/similar#similar)
-which generalizes CSR/CSC to n-dimensional arrays. Like their
-two-dimensional CSR/CSC counterparts, GCXS arrays compress well. Whereas
-the storage cost of COO depends heavily on the number of dimensions of
-the array, the number of dimensions only minimally affects the storage
-cost of GCXS arrays, which results in favorable compression ratios
-across many use cases.
+ {width=10%, align=left}
+
API
+ { .card }
-Together these formats cover a wide array of applications of sparsity.
-Additionally, with each format complying with the
-[`numpy.ndarray`][] interface and following
-the appropriate dispatching protocols, pydata/sparse arrays can interact
-with other array libraries and seamlessly take part in
-pydata-ecosystem-based workflows.
-## LICENSE
+ {width=10%, align=left}
+
Contributing
+ { .card }
-This library is licensed under BSD-3.
+
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n",
+ "