The identity matrix is a matrix with ones on the leading diagonal, and zeros everywhere else. It's commonly denoted by the letter I if the size is evident from the context.
$$
I = \begin{bmatrix}
1 & 0 \\
0 & 1
\end{bmatrix}
$$
It can also be denoted by In to refer more specifically to an $n$ by $n$ identity matrix.
$$
I_2 = \begin{bmatrix}
1 & 0 \\
0 & 1
\end{bmatrix}
\\
I_3 = \begin{bmatrix}
1 & 0 & 0 \\
0 & 1 & 0 \\
0 & 0 & 1
\end{bmatrix}
$$
It's significant in linear algebra because any matrix multiplied by its identity matrix results in the same as the original.
$$
\begin{bmatrix}
5 & 18 \\
7 & 22
\end{bmatrix}
\cdot
\begin{bmatrix}
1 & 0 \\
0 & 1
\end{bmatrix}
=
\begin{bmatrix}
5 & 18 \\
7 & 22
\end{bmatrix}
$$
Numpy
Implementing in Numpy is trivial and easy to remember.
