What is Sparse Data

Sparse data is data that has mostly unused elements (elements that don't carry any information ).

It can be an array like this one:

[1, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0]

Sparse Data: is a data set where most of the item values are zero.

Dense Array: is the opposite of a sparse array: most of the values are not zero.

In scientific computing, when we are dealing with partial derivatives in linear algebra we will come across sparse data.

How to Work With Sparse Data

SciPy has a module, scipy.sparse that provides functions to deal with sparse data.

There are primarily two types of sparse matrices that we use:

CSC - Compressed Sparse Column. For efficient arithmetic, fast column slicing.

CSR - Compressed Sparse Row. For fast row slicing, faster matrix vector products

We will use the CSR matrix in this tutorial.

CSR Matrix

We can create CSR matrix by passing an arrray into function scipy.sparse.csr_matrix() .

Example

Create a CSR matrix from an array:

import numpy as np
from scipy.sparse import csr_matrix arr = np.array([0, 0, 0, 0, 0, 1, 1, 0, 2]) print(csr_matrix(arr)) Try it Yourself »

The example above returns:

From the result we can see that there are 3 items with value.

The 1. item is in row 0 position 5 and has the value

The 2. item is in row 0 position 6 and has the value

The 3. item is in row 0 position 8 and has the value

Exercise:

Insert the missing method to print the number of values in the array that are NOT zeros:

import numpy as np from scipy.sparse import csr_matrix arr = np.array([[0, 0, 0], [0, 0, 1], [1, 0, 2]]) print(csr_matrix(arr). ()) Submit Answer »

Start the Exercise

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