SciPy Interview Questions and Answers
Experienced / Expert level questions & answers
Ques 1. How can you perform image processing using SciPy?
The `scipy.ndimage` module in SciPy is used for image processing tasks, including filtering, morphology, and measurements. It provides functions to work with n-dimensional images.
Ques 2. Explain the difference between `scipy.linalg.eig` and `scipy.linalg.eigh`.
`scipy.linalg.eig` calculates eigenvalues and eigenvectors for a general matrix, while `scipy.linalg.eigh` is specific to Hermitian or real symmetric matrices, providing a more efficient algorithm for such cases.
Ques 3. How can you solve ordinary differential equations (ODEs) using `scipy.integrate`?
The `scipy.integrate.solve_ivp` function is used to solve initial value problems for ordinary differential equations. It supports a variety of ODE solvers and can handle both stiff and non-stiff problems.
Ques 4. Explain the use of `scipy.optimize.least_squares`.
`scipy.optimize.least_squares` is used for solving nonlinear least squares problems. It minimizes the sum of squared differences between observed and predicted values.
Ques 5. How can you perform sparse matrix factorization using `scipy.sparse.linalg.svds`?
`scipy.sparse.linalg.svds` is used for sparse matrix factorization using the singular value decomposition (SVD) method. It is efficient for large, sparse matrices.
Ques 6. What is the purpose of `scipy.optimize.root`?
`scipy.optimize.root` is used for finding the roots (zeros) of a system of nonlinear equations. It supports both univariate and multivariate root-finding.
Ques 7. How does `scipy.spatial.Delaunay` work?
`scipy.spatial.Delaunay` is used for computing the Delaunay triangulation of a set of points in the plane. It is often used in applications like mesh generation and interpolation.
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