QChem - 1 year ago 192
Python Question

# How to know when to use numpy.linalg instead of scipy.linalg?

`scipy.linalg`
over
`nump.linalg`
functions. For doing linear algebra, ideally (and conveniently) I would like to combine the functionalities of
`numpy.array`
and
`scipy.linalg`
without ever looking towards
`numpy.linalg`
. This is not always possible and may become too frustrating.

Is there a comparative checklist of equivalent functions from these two modules to quickly determine when to use
`numpy.linalg`
in case a function is absent in
`scipy.linalg`
?

e.g. There are
`scipy.linalg.norm()`
and
`numpy.linalg.norm()`
, but there seem to be no scipy equivalents of
`numpy.linalg.matrix_rank()`
and
`numpy.linalg.cond()`
.

So, the normal rule is to just use `scipy.linalg` as it generally supports all of the `numpy.linalg` functionality and more. The documentation says this:

`numpy.linalg` for more linear algebra functions. Note that although `scipy.linalg` imports most of them, identically named functions from `scipy.linalg` may offer more or slightly differing functionality.

However, `matrix_rank()` is only in NumPy.

Here we can see the differences between the functions provided by both libraries, and how SciPy is more complete:

``````In [2]: from scipy import linalg as scipy_linalg
In [3]: from numpy import linalg as numpy_linalg
In [4]: dir(scipy_linalg)
Out[4]:
[
...
'absolute_import',
'basic',
'bench',
'blas',
'block_diag',
'cho_factor',
'cho_solve',
'cho_solve_banded',
'cholesky',
'cholesky_banded',
'circulant',
'companion',
'coshm',
'cosm',
'cython_blas',
'cython_lapack',
'decomp',
'decomp_cholesky',
'decomp_lu',
'decomp_qr',
'decomp_schur',
'decomp_svd',
'det',
'dft',
'diagsvd',
'division',
'eig',
'eig_banded',
'eigh',
'eigvals',
'eigvals_banded',
'eigvalsh',
'expm',
'expm2',
'expm3',
'expm_cond',
'expm_frechet',
'find_best_blas_type',
'flinalg',
'fractional_matrix_power',
'funm',
'get_blas_funcs',
'get_lapack_funcs',
'hankel',
'helmert',
'hessenberg',
'hilbert',
'inv',
'invhilbert',
'invpascal',
'kron',
'lapack',
'leslie',
'linalg_version',
'logm',
'lstsq',
'lu',
'lu_factor',
'lu_solve',
'matfuncs',
'misc',
'norm',
'ordqz',
'orth',
'orthogonal_procrustes',
'pascal',
'pinv',
'pinv2',
'pinvh',
'polar',
'print_function',
'qr',
'qr_delete',
'qr_insert',
'qr_multiply',
'qr_update',
'qz',
'rq',
'rsf2csf',
's',
'schur',
'signm',
'sinhm',
'sinm',
'solve',
'solve_banded',
'solve_circulant',
'solve_continuous_are',
'solve_discrete_are',
'solve_discrete_lyapunov',
'solve_lyapunov',
'solve_sylvester',
'solve_toeplitz',
'solve_triangular',
'solveh_banded',
'special_matrices',
'sqrtm',
'svd',
'svdvals',
'tanhm',
'tanm',
'test',
'toeplitz',
'tri',
'tril',
'triu']

In [5]: dir(numpy_linalg)
Out[5]:
[
...
'absolute_import',
'bench',
'cholesky',
'cond',
'det',
'division',
'eig',
'eigh',
'eigvals',
'eigvalsh',
'info',
'inv',
'lapack_lite',
'linalg',
'lstsq',
'matrix_power',
'matrix_rank',
'multi_dot',
'norm',
'pinv',
'print_function',
'qr',
'slogdet',
'solve',
'svd',
'tensorinv',
'tensorsolve',
'test']

In [6]:
``````

Note that not all of these are functions.

SciPy does provide `scipy.linalg.expm_cond()`, but this only returns the condition in the Frobenius norm, whereas `numpy.linalg.cond()` supports multiple norms.

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