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Reshape

How to change the dimensions of a NumPy array.

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Imports

This tutorial imports Numpy.

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

import numpy as np

Reshape an Array

.reshape() is a numpy array method that is used to reconstruct the given array into another shape with different dimensions. For a 1D array of the form

$$ \begin{align*} [x_1, x_2, ... , x_L] \end{align*} $$

of length $L$ we can reshape into an matrix-looking array with $R$ rows and $C$ columns where

$$ \begin{align*} R \times C = L \end{align*} $$

For example the array $[1, 2, 3, 4, 5, 6]$ could be reshaped into this matrix-like array (i.e. just an array of arrays) with 2 rows and and 3 columns:

$$ \begin{align*} [[1, 2, 3], [4, 5, 6]] \end{align*} $$
In [2]:
import plotly.plotly as py
from plotly.tools import FigureFactory as FF

z = np.arange(10).reshape((2, 5))

fig = FF.create_annotated_heatmap(z, colorscale='Viridis')
py.iplot(fig, filename='numpy-reshape-1')
Out[2]:
In [3]:
import plotly.plotly as py
from plotly.tools import FigureFactory as FF

z = np.arange(10).reshape((5, 2))

fig = FF.create_annotated_heatmap(z, colorscale='Viridis')
py.iplot(fig, filename='numpy-reshape-2')
Out[3]:
In [4]:
import plotly.plotly as py
from plotly.tools import FigureFactory as FF

z = [np.arange(10).reshape(10)]

fig = FF.create_annotated_heatmap(z, colorscale='Viridis')
py.iplot(fig, filename='numpy-reshape-3')
Out[4]:
In [5]:
help(np.reshape)
Help on function reshape in module numpy.core.fromnumeric:

reshape(a, newshape, order='C')
    Gives a new shape to an array without changing its data.
    
    Parameters
    ----------
    a : array_like
        Array to be reshaped.
    newshape : int or tuple of ints
        The new shape should be compatible with the original shape. If
        an integer, then the result will be a 1-D array of that length.
        One shape dimension can be -1. In this case, the value is inferred
        from the length of the array and remaining dimensions.
    order : {'C', 'F', 'A'}, optional
        Read the elements of `a` using this index order, and place the elements
        into the reshaped array using this index order.  'C' means to
        read / write the elements using C-like index order, with the last axis
        index changing fastest, back to the first axis index changing slowest.
        'F' means to read / write the elements using Fortran-like index 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 indexing.  'A'
        means to read / write the elements in Fortran-like index order if `a`
        is Fortran *contiguous* in memory, C-like order otherwise.
    
    Returns
    -------
    reshaped_array : ndarray
        This will be a new view object if possible; otherwise, it will
        be a copy.  Note there is no guarantee of the *memory layout* (C- or
        Fortran- contiguous) of the returned array.
    
    See Also
    --------
    ndarray.reshape : Equivalent method.
    
    Notes
    -----
    It is not always possible to change the shape of an array without
    copying the data. If you want an error to be raise if the data is copied,
    you should assign the new shape to the shape attribute of the array::
    
     >>> a = np.zeros((10, 2))
     # A transpose make the array non-contiguous
     >>> b = a.T
     # Taking a view makes it possible to modify the shape without modifying
     # the initial object.
     >>> c = b.view()
     >>> c.shape = (20)
     AttributeError: incompatible shape for a non-contiguous array
    
    The `order` keyword gives the index ordering both for *fetching* the values
    from `a`, and then *placing* the values into the output array.
    For example, let's say you have an array:
    
    >>> a = np.arange(6).reshape((3, 2))
    >>> a
    array([[0, 1],
           [2, 3],
           [4, 5]])
    
    You can think of reshaping as first raveling the array (using the given
    index order), then inserting the elements from the raveled array into the
    new array using the same kind of index ordering as was used for the
    raveling.
    
    >>> np.reshape(a, (2, 3)) # C-like index ordering
    array([[0, 1, 2],
           [3, 4, 5]])
    >>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
    array([[0, 1, 2],
           [3, 4, 5]])
    >>> np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
    array([[0, 4, 3],
           [2, 1, 5]])
    >>> np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
    array([[0, 4, 3],
           [2, 1, 5]])
    
    Examples
    --------
    >>> a = np.array([[1,2,3], [4,5,6]])
    >>> np.reshape(a, 6)
    array([1, 2, 3, 4, 5, 6])
    >>> np.reshape(a, 6, order='F')
    array([1, 4, 2, 5, 3, 6])
    
    >>> np.reshape(a, (3,-1))       # the unspecified value is inferred to be 2
    array([[1, 2],
           [3, 4],
           [5, 6]])

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