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Empty

An Empty NumPy array is an array filled with only zero or near-zero values. The arrays can be of any shape.

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Imports

This tutorial imports Plotly and Numpy.

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

import numpy as np

Empty Array

The np.empty() function is used to create a 2D array filled with all zeros. Often the array is filled with values near zero because of the way memory and RAM works in the computer.

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

shape = (4, 6)

zeros_array = np.empty(shape)

colorscale = [[0, 'rgb(49, 52, 92)'], [1, 'rgb(49, 52, 92)']]
font_colors = ['rgb(255, 255, 255)']

table = FF.create_table(zeros_array, colorscale, font_colors)
py.iplot(table, filename='numpy-empty')
Out[2]:
In [2]:
help(np.empty)
Help on built-in function empty in module numpy.core.multiarray:

empty(...)
    empty(shape, dtype=float, order='C')
    
    Return a new array of given shape and type, without initializing entries.
    
    Parameters
    ----------
    shape : int or tuple of int
        Shape of the empty array
    dtype : data-type, optional
        Desired output data-type.
    order : {'C', 'F'}, optional
        Whether to store multi-dimensional data in row-major
        (C-style) or column-major (Fortran-style) order in
        memory.
    
    Returns
    -------
    out : ndarray
        Array of uninitialized (arbitrary) data of the given shape, dtype, and
        order.  Object arrays will be initialized to None.
    
    See Also
    --------
    empty_like, zeros, ones
    
    Notes
    -----
    `empty`, unlike `zeros`, does not set the array values to zero,
    and may therefore be marginally faster.  On the other hand, it requires
    the user to manually set all the values in the array, and should be
    used with caution.
    
    Examples
    --------
    >>> np.empty([2, 2])
    array([[ -9.74499359e+001,   6.69583040e-309],
           [  2.13182611e-314,   3.06959433e-309]])         #random
    
    >>> np.empty([2, 2], dtype=int)
    array([[-1073741821, -1067949133],
           [  496041986,    19249760]])                     #random

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