array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0)

Create an array.

Parameters
----------
object : array_like
    An array, any object exposing the array interface, an
    object whose __array__ method returns an array, or any
    (nested) sequence.(more...)

zeros(shape, dtype=float, order='C')

Return a new array of given shape and type, filled with zeros.

Parameters
----------
shape : int or sequence of ints
    Shape of the new array, e.g., ``(2, 3)`` or ``2``.
dtype : data-type, optional
    The desired data-type for the array, e.g., `numpy.int8`.  Default is(more...)

Common test support for all numpy test scripts.

This single module should provide all the common functionality for numpy tests
in a single location, so that test scripts can just import it and work right
away.

Defines a multi-dimensional array and useful procedures for Numerical computation.

Functions

-   array                      - NumPy Array construction
-   zeros                      - Return an array of all zeros
-   empty                      - Return an unitialized array
-   shape                      - Return shape of sequence or array
-   rank                       - Return number of dimensions
-   size                       - Return number of elements in entire array or a(more...)

No Document.

========================
Random Number Generation
========================

==================== =========================================================
Utility functions
==============================================================================
random               Uniformly distributed values of a given shape.
bytes                Uniformly distributed random bytes.
random_integers      Uniformly distributed integers in a given range.(more...)

dot(a, b)

Dot product of two arrays.

For 2-D arrays it is equivalent to matrix multiplication, and for 1-D
arrays to inner product of vectors (without complex conjugation). For
N dimensions it is a sum product over the last axis of `a` and
the second-to-last of `b`::

    dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])(more...)

arange([start,] stop[, step,], dtype=None)

Return evenly spaced values within a given interval.

Values are generated within the half-open interval ``[start, stop)``
(in other words, the interval including `start` but excluding `stop`).
For integer arguments the function is equivalent to the Python built-in
`range <http://docs.python.org/lib/built-in-funcs.html>`_ function,
but returns a ndarray rather than a list.
(more...)

sqrt(x[, out])

Return the positive square-root of an array, element-wise.

Parameters
----------
x : array_like
    The values whose square-roots are required.
out : ndarray, optional
    Alternate array object in which to put the result; if provided, it(more...)

Enhanced distutils with Fortran compilers support and more.

Return a new array of given shape and type, filled with ones.

Please refer to the documentation for `zeros` for further details.

See Also
--------
zeros, ones_like

Examples
--------(more...)

Sum of array elements over a given axis.

Parameters
----------
a : array_like
    Elements to sum.
axis : integer, optional
    Axis over which the sum is taken. By default `axis` is None,
    and all elements are summed.
dtype : dtype, optional(more...)

Convert the input to an array.

Parameters
----------
a : array_like
    Input data, in any form that can be converted to an array.  This
    includes lists, lists of tuples, tuples, tuples of tuples, tuples
    of lists and ndarrays.
dtype : data-type, optional
    By default, the data-type is inferred from the input data.(more...)

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(more...)

exp(x[, out])

Calculate the exponential of all elements in the input array.

Parameters
----------
x : array_like
    Input values.

Returns(more...)

sin(x[, out])

Trigonometric sine, element-wise.

Parameters
----------
x : array_like
    Angle, in radians (:math:`2 \pi` rad equals 360 degrees).

Returns(more...)

log(x[, out])

Natural logarithm, element-wise.

The natural logarithm `log` is the inverse of the exponential function,
so that `log(exp(x)) = x`. The natural logarithm is logarithm in base `e`.

Parameters
----------
x : array_like(more...)

=============
Masked Arrays
=============

Arrays sometimes contain invalid or missing data.  When doing operations
on such arrays, we wish to suppress invalid values, which is the purpose masked
arrays fulfill (an example of typical use is given below).

For example, examine the following array:
(more...)

where(condition, [x, y])

Return elements, either from `x` or `y`, depending on `condition`.

If only `condition` is given, return ``condition.nonzero()``.

Parameters
----------
condition : array_like, bool
    When True, yield `x`, otherwise yield `y`.(more...)

cos(x[, out])

Cosine elementwise.

Parameters
----------
x : array_like
    Input array in radians.
out : ndarray, optional
    Output array of same shape as `x`.(more...)