Numpy 2D Arrays

This note covers matrices in Numpy using 2D arrays.

• Shape and dimensions
• Row/column slicing
• Axis-based operations
• Reshape basics

Base Example

matrix = np.array([
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9],
])
 
matrix.shape   # (3, 3)
matrix.ndim    # 2
matrix.size    # 9

2D Array Methods

Shape and Structure

matrix.T                  # transpose
matrix.reshape(1, 9)
matrix.reshape(9, 1)
matrix.flatten()          # 1D copy
matrix.ravel()            # 1D view when possible
matrix.astype(float)

Row/Column Selection

matrix[0, :]              # first row
matrix[:, 0]              # first column
matrix[0:2, 1:3]          # submatrix
matrix[[0, 2], :]         # select rows by index list
matrix[:, [0, 2]]         # select columns by index list
matrix[matrix > 4]        # boolean indexing

Aggregation with axis

matrix.sum()              # all elements
matrix.sum(axis=0)        # per column
matrix.sum(axis=1)        # per row
matrix.mean(axis=0)
matrix.min(axis=1)
matrix.argmax(axis=1)

Matrix Operations

matrix + 10
matrix * 2
matrix1 * matrix2         # element-wise
matrix1 @ matrix2         # matrix multiplication
np.dot(matrix1, matrix2)  # same idea for 2D arrays

Joining and Splitting

np.concatenate((a, b), axis=0)  # stack rows
np.concatenate((a, b), axis=1)  # stack columns
np.vstack((a, b))
np.hstack((a, b))

For multidimensional indexing and advanced slice patterns, see Multidimensional Slicing.