Review of Matrices

Review of Matrices#

Like vectors, matrices are also a type of numpy array. Unlike vectors—which consist of data organized in a single dimension—matrices consist of data organized along two dimensions in a grid.
Like all numpy arrays, matrices are homogenously typed, meaning the data they hold must always be of the same type.
All numpy arrays consist of a single-dimensional string of data and information on how that data should be “folded” to create an array. A vector is just data that is not folded, while a matrix is data that is folded into a grid.
The shape of an array can be found in the .shape attribute.
How an array is folded can be modified with the .reshape() method.

Subsetting matrices is just like subsetting vectors, except with two entries between the square brackets instead of one: [ , ].
The first entry in the square brackets relates to a location along the x-axis (rows), the second to the y-axis (columns).
You must always pass two locations to subset a matrix. If you want all rows or all columns, simply pass a : (e.g., to get all of the columns in the first row, you would pass my_matrix[0, :]).
Like vectors, you can subset using simple indexing using index values or ranges. This will return a view.
Like vectors, you can also subset with fancy indexing or a Boolean vector.
You can mix how you subset, and use a Boolean for rows and an index for columns.
Subsetting on both rows and columns allows you to edit matrices in very powerful ways.