### Table Of Contents

- API - importing from Scipy
  - Guidelines for importing functions from Scipy
  - API definition

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# API - importing from Scipy

In Python the distinction between what is the public API of a library and what are private implementation details is not always clear. Unlike in other languages like Java, it is possible in Python to access “private” function or objects. Occasionally this may be convenient, but be aware that if you do so your code may break without warning in future releases. Some widely understood rules for what is and isn’t public in Python are:

> - Methods / functions / classes and module attributes whose names begin with a leading underscore are private.
> - If a class name begins with a leading underscore none of its members are public, whether or not they begin with a leading underscore.
> - If a module name in a package begins with a leading underscore none of its members are public, whether or not they begin with a leading underscore.
> - If a module or package defines `__all__` that authoritatively defines the public interface.
> - If a module or package doesn’t define `__all__` then all names that don’t start with a leading underscore are public.

Note

Reading the above guidelines one could draw the conclusion that every private module or object starts with an underscore. This is not the case; the presence of underscores do mark something as private, but the absence of underscores do not mark something as public.

In Scipy there are modules whose names don’t start with an underscore, but that should be considered private. To clarify which modules these are we define below what the public API is for Scipy, and give some recommendations for how to import modules/functions/objects from Scipy.

## Guidelines for importing functions from Scipy

The scipy namespace itself only contains functions imported from numpy. These functions still exist for backwards compatibility, but should be imported from numpy directly.

Everything in the namespaces of scipy submodules is public. In general, it is recommended to import functions from submodule namespaces. For example, the function `curve_fit` (defined in scipy/optimize/minpack.py) should be imported like this:

    from scipy import optimize
    result = optimize.curve_fit(...)

This form of importing submodules is preferred for all submodules except `scipy.io` (because `io` is also the name of a module in the Python stdlib):

    from scipy import interpolate
    from scipy import integrate
    import scipy.io as spio

In some cases, the public API is one level deeper. For example the `scipy.sparse.linalg` module is public, and the functions it contains are not available in the `scipy.sparse` namespace. Sometimes it may result in more easily understandable code if functions are imported from one level deeper. For example, in the following it is immediately clear that `lomax` is a distribution if the second form is chosen:

    # first form
    from scipy import stats
    stats.lomax(...)

    # second form
    from scipy.stats import distributions
    distributions.lomax(...)

In that case the second form can be chosen, if it is documented in the next section that the submodule in question is public.

## API definition

Every submodule listed below is public. That means that these submodules are unlikely to be renamed or changed in an incompatible way, and if that is necessary a deprecation warning will be raised for one Scipy release before the change is made.

- scipy.cluster
  - vq
  - hierarchy
- scipy.constants
- scipy.fftpack
- scipy.integrate
- scipy.interpolate
- scipy.io
  - arff
  - harwell_boeing
  - idl
  - matlab
  - netcdf
  - wavfile
- scipy.linalg
  - scipy.linalg.blas
  - scipy.linalg.cython_blas
  - scipy.linalg.lapack
  - scipy.linalg.cython_lapack
  - scipy.linalg.interpolative
- scipy.misc
- scipy.ndimage
- scipy.odr
- scipy.optimize
- scipy.signal
- scipy.sparse
  - linalg
  - csgraph
- scipy.spatial
  - distance
- scipy.special
- scipy.stats
  - distributions
  - mstats