numpy学习

https://docs.scipy.org/doc/numpy/reference/
https://docs.scipy.org/doc/numpy/numpy-ref-1.13.0.pdf
https://docs.scipy.org/doc/numpy-dev/contents.html

help(np.linalg)
Help on package numpy.linalg in numpy:

NAME
    numpy.linalg

DESCRIPTION
    Core Linear Algebra Tools
    -------------------------
    Linear algebra basics:

    - norm            Vector or matrix norm
    - inv             Inverse of a square matrix
    - solve           Solve a linear system of equations
    - det             Determinant of a square matrix
    - lstsq           Solve linear least-squares problem
    - pinv            Pseudo-inverse (Moore-Penrose) calculated using a singular
                      value decomposition
    - matrix_power    Integer power of a square matrix

    Eigenvalues and decompositions:

    - eig             Eigenvalues and vectors of a square matrix
    - eigh            Eigenvalues and eigenvectors of a Hermitian matrix
    - eigvals         Eigenvalues of a square matrix
    - eigvalsh        Eigenvalues of a Hermitian matrix
    - qr              QR decomposition of a matrix
    - svd             Singular value decomposition of a matrix
    - cholesky        Cholesky decomposition of a matrix

    Tensor operations:

    - tensorsolve     Solve a linear tensor equation
    - tensorinv       Calculate an inverse of a tensor

    Exceptions:

    - LinAlgError     Indicates a failed linear algebra operation

PACKAGE CONTENTS
    _umath_linalg
    info
    lapack_lite
    linalg
    setup

DATA
    absolute_import = _Feature((2, 5, 0, 'alpha', 1), (3, 0, 0, 'alpha', 0...
    division = _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192...
    print_function = _Feature((2, 6, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0)...

FILE
    /opt/anaconda3/lib/python3.6/site-packages/numpy/linalg/__init__.py

sorted(np.__all__)
Out[17]:
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