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How to flatten the elements of a NumPy array into a 1D NumPy array.

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This tutorial imports Plotly and Numpy.

In [1]:
import plotly.plotly as py
from import FigureFactory as FF

import numpy as np


np.ravel() is used to "flatten" an ND-array by extracting all its values and returning a 1D-array of all the values from the original.

In [2]:
import plotly.plotly as py
from import FigureFactory as FF

pi_array = np.array([[3, 1, 4, 1],
                     [5, 9, 2, 6],
                     [5, 3, 5, 8],
                     [9, 7, 9, 3]])

ravel_array = np.ravel(pi_array)

fig = FF.create_annotated_heatmap([ravel_array], colorscale='Earth' )
fig.layout.title = '$\pi$'
py.iplot(fig, filename='numpy-ravel-pi')
In [2]:
Help on function ravel in module numpy.core.fromnumeric:

ravel(a, order='C')
    Return a contiguous flattened array.
    A 1-D array, containing the elements of the input, is returned.  A copy is
    made only if needed.
    As of NumPy 1.10, the returned array will have the same type as the input
    array. (for example, a masked array will be returned for a masked array
    a : array_like
        Input array.  The elements in `a` are read in the order specified by
        `order`, and packed as a 1-D array.
    order : {'C','F', 'A', 'K'}, optional
        The elements of `a` are read using this index order. 'C' means
        to index the elements in row-major, C-style order,
        with the last axis index changing fastest, back to the first
        axis index changing slowest.  'F' means to index the elements
        in column-major, Fortran-style order, with the
        first index changing fastest, and the last index changing
        slowest. Note that the 'C' and 'F' options take no account of
        the memory layout of the underlying array, and only refer to
        the order of axis indexing.  'A' means to read the elements in
        Fortran-like index order if `a` is Fortran *contiguous* in
        memory, C-like order otherwise.  'K' means to read the
        elements in the order they occur in memory, except for
        reversing the data when strides are negative.  By default, 'C'
        index order is used.
    y : array_like
        If `a` is a matrix, y is a 1-D ndarray, otherwise y is an array of
        the same subtype as `a`. The shape of the returned array is
        ``(a.size,)``. Matrices are special cased for backward
    See Also
    ndarray.flat : 1-D iterator over an array.
    ndarray.flatten : 1-D array copy of the elements of an array
                      in row-major order.
    ndarray.reshape : Change the shape of an array without changing its data.
    In row-major, C-style order, in two dimensions, the row index
    varies the slowest, and the column index the quickest.  This can
    be generalized to multiple dimensions, where row-major order
    implies that the index along the first axis varies slowest, and
    the index along the last quickest.  The opposite holds for
    column-major, Fortran-style index ordering.
    When a view is desired in as many cases as possible, ``arr.reshape(-1)``
    may be preferable.
    It is equivalent to ``reshape(-1, order=order)``.
    >>> x = np.array([[1, 2, 3], [4, 5, 6]])
    >>> print(np.ravel(x))
    [1 2 3 4 5 6]
    >>> print(x.reshape(-1))
    [1 2 3 4 5 6]
    >>> print(np.ravel(x, order='F'))
    [1 4 2 5 3 6]
    When ``order`` is 'A', it will preserve the array's 'C' or 'F' ordering:
    >>> print(np.ravel(x.T))
    [1 4 2 5 3 6]
    >>> print(np.ravel(x.T, order='A'))
    [1 2 3 4 5 6]
    When ``order`` is 'K', it will preserve orderings that are neither 'C'
    nor 'F', but won't reverse axes:
    >>> a = np.arange(3)[::-1]; a
    array([2, 1, 0])
    >>> a.ravel(order='C')
    array([2, 1, 0])
    >>> a.ravel(order='K')
    array([2, 1, 0])
    >>> a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a
    array([[[ 0,  2,  4],
            [ 1,  3,  5]],
           [[ 6,  8, 10],
            [ 7,  9, 11]]])
    >>> a.ravel(order='C')
    array([ 0,  2,  4,  1,  3,  5,  6,  8, 10,  7,  9, 11])
    >>> a.ravel(order='K')
    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])