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Array

A NumPy array is like a Python list and is handled very similarly.

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Imports

This tutorial imports Plotly and Numpy.

In [1]:
import plotly.plotly as py
import plotly.graph_objs as go

import numpy as np

Create an Array

Very similar to the Python list object, a numpy array is an array for which data can be appended, removed, and can be reshaped. The data can be read according to other programming languages (eg. C, Fortran) and instantiated as all-zeros or as an empty array.

In [2]:
import plotly.plotly as py
import plotly.graph_objs as go

x = np.array([1, 2, 3])
y = np.array([4, 7, 2])

trace = go.Scatter(x=x, y=y)
py.iplot([trace], filename='numpy-array-ex1')
Out[2]:

Create an N-D Array

np.ndarray creates an array of a given shape and fills the array with garbage values.

In [3]:
import plotly.plotly as py
import plotly.graph_objs as go

nd_array = np.ndarray(shape=(2,3), dtype=float)
nd_array[0] = x
nd_array[1] = y

trace = go.Scatter(x=nd_array[0], y=nd_array[1])
py.iplot([trace], filename='numpy-ndarray-ex2')
Out[3]:
In [4]:
help(np.array)
Help on built-in function array in module numpy.core.multiarray:

array(...)
    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.
    dtype : data-type, optional
        The desired data-type for the array.  If not given, then
        the type will be determined as the minimum type required
        to hold the objects in the sequence.  This argument can only
        be used to 'upcast' the array.  For downcasting, use the
        .astype(t) method.
    copy : bool, optional
        If true (default), then the object is copied.  Otherwise, a copy
        will only be made if __array__ returns a copy, if obj is a
        nested sequence, or if a copy is needed to satisfy any of the other
        requirements (`dtype`, `order`, etc.).
    order : {'C', 'F', 'A'}, optional
        Specify the order of the array.  If order is 'C', then the array
        will be in C-contiguous order (last-index varies the fastest).
        If order is 'F', then the returned array will be in
        Fortran-contiguous order (first-index varies the fastest).
        If order is 'A' (default), then the returned array may be
        in any order (either C-, Fortran-contiguous, or even discontiguous),
        unless a copy is required, in which case it will be C-contiguous.
    subok : bool, optional
        If True, then sub-classes will be passed-through, otherwise
        the returned array will be forced to be a base-class array (default).
    ndmin : int, optional
        Specifies the minimum number of dimensions that the resulting
        array should have.  Ones will be pre-pended to the shape as
        needed to meet this requirement.
    
    Returns
    -------
    out : ndarray
        An array object satisfying the specified requirements.
    
    See Also
    --------
    empty, empty_like, zeros, zeros_like, ones, ones_like, fill
    
    Examples
    --------
    >>> np.array([1, 2, 3])
    array([1, 2, 3])
    
    Upcasting:
    
    >>> np.array([1, 2, 3.0])
    array([ 1.,  2.,  3.])
    
    More than one dimension:
    
    >>> np.array([[1, 2], [3, 4]])
    array([[1, 2],
           [3, 4]])
    
    Minimum dimensions 2:
    
    >>> np.array([1, 2, 3], ndmin=2)
    array([[1, 2, 3]])
    
    Type provided:
    
    >>> np.array([1, 2, 3], dtype=complex)
    array([ 1.+0.j,  2.+0.j,  3.+0.j])
    
    Data-type consisting of more than one element:
    
    >>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')])
    >>> x['a']
    array([1, 3])
    
    Creating an array from sub-classes:
    
    >>> np.array(np.mat('1 2; 3 4'))
    array([[1, 2],
           [3, 4]])
    
    >>> np.array(np.mat('1 2; 3 4'), subok=True)
    matrix([[1, 2],
            [3, 4]])

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