| np.array() | Creates a new NumPy array. |
| np.zeros() | Returns a new array of given shape and type, filled with zeros. |
| np.ones() | Returns a new array of given shape and type, filled with ones. |
| np.arange() | Returns evenly spaced values within a given interval. |
| np.linspace() | Returns evenly spaced numbers over a specified interval. |
| np.reshape() | Gives a new shape to an array without changing its data. |
| np.append() | Appends values to the end of an array. |
| np.concatenate() | Joins a sequence of arrays along an existing axis. |
| np.sum() | Sum of array elements over a given axis. |
| np.mean() | Computes the arithmetic mean along the specified axis. |
| np.median() | Computes the median along the specified axis. |
| np.std() | Computes the standard deviation along the specified axis. |
| np.var() | Computes the variance along the specified axis. |
| np.min() | Returns the minimum of an array or minimum along an axis. |
| np.max() | Returns the maximum of an array or maximum along an axis. |
| np.dot() | Dot product of two arrays. |
| np.cross() | Cross product of two arrays. |
| np.sort() | Returns a sorted copy of an array. |
| np.argsort() | Returns the indices that would sort an array. |
| np.unique() | Finds the unique elements of an array. |
| np.linalg.inv() | Computes the multiplicative inverse of a matrix. |
| np.linalg.det() | Computes the determinant of an array. |
| np.linalg.eig() | Computes the eigenvalues and right eigenvectors of a square array. |
| np.linalg.eigh() | Computes the eigenvalues and eigenvectors of a Hermitian or symmetric matrix. |
| np.linalg.solve() | Solves a linear matrix equation, or system of linear scalar equations. |
| np.linalg.svd() | Performs singular value decomposition. |
| np.linalg.pinv() | Computes the Moore-Penrose pseudoinverse of a matrix. |
| np.linalg.norm() | Computes the norm of a vector or matrix. |
| np.linalg.qr() | Computes the QR decomposition of a matrix. |
| np.linalg.cholesky() | Computes the Cholesky decomposition of a matrix. |