# Python Scikit learn: Create a 2-D array with ones on the diagonal and zeros elsewhere

## Python Machine learning Iris Basic: Exercise-4 with Solution

Write a Python program to create a 2-D array with ones on the diagonal and zeros elsewhere. Now convert the NumPy array to a SciPy sparse matrix in CSR format.

From wikipedia :

In numerical analysis and scientific computing, a sparse matrix or sparse array is a matrix in which most of the elements are zero. By contrast, if most of the elements are nonzero, then the matrix is considered dense. The number of zero-valued elements divided by the total number of elements (e.g., m × n for an m × n matrix) is called the sparsity of the matrix (which is equal to 1 minus the density of the matrix). Using those definitions, a matrix will be sparse when its sparsity is greater than 0.5.

**Sample Solution:**

**Python Code:**

```
import numpy as np
from scipy import sparse
eye = np.eye(4)
print("NumPy array:\n", eye)
sparse_matrix = sparse.csr_matrix(eye)
print("\nSciPy sparse CSR matrix:\n", sparse_matrix)
```

Sample Output:

NumPy array: [[1. 0. 0. 0.] [0. 1. 0. 0.] [0. 0. 1. 0.] [0. 0. 0. 1.]] SciPy sparse CSR matrix: (0, 0) 1.0 (1, 1) 1.0 (2, 2) 1.0 (3, 3) 1.0

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