Array manipulation routines
reshape(a, newshape)
ravel(a)
ndarray.flatten()
Gives a new shape to an array without changing its data.
Parameters:
x : array_like Angle to be reshaped.
Returns:
reshaped_array.
Example:
Python
>>> import numpy
>>> a = numpy.arange(6).reshape([3, 2])
>>> a
array([[0, 1],
[2, 3],
[4, 5]])
Javascript with PyExtJS
> a = numpy.arange(6).reshape([3, 2]);
[[0, 1], [2, 3], [4, 5]]
> a = numpy.arange(6).reshape(3, 2); // <----- It works without brackets also
[[0, 1], [2, 3], [4, 5]]
Return a contiguous flattened array.
Parameters:
x : array_like input array. The elements in a are read in the order specified by order, and packed as a 1-D array.
Returns:
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 compatibility.
Example:
Python
>>> import numpy
>>> a = numpy.arange(6).reshape(3, 2)
>>> numpy.ravel(a)
array([0, 1, 2, 3, 4, 5])
Javascript with PyExtJS
> a = numpy.arange(6).reshape(3, 2);
[[0, 1], [2, 3], [4, 5]]
> numpy.ravel(a);
[0, 1, 2, 3, 4, 5]
Return a copy of the array collapsed into one dimension.
Parameters:
none.
Returns:
y : array_like. A copy of the input array, flattened to one dimension.
Example:
Python
>>> import numpy
>>> a = numpy.arange(6).reshape(3, 2)
>>> a.flatten()
array([0, 1, 2, 3, 4, 5])
Javascript with PyExtJS
> a = numpy.arange(6).reshape(3, 2);
[[0, 1], [2, 3], [4, 5]]
> a.flatten();
[0, 1, 2, 3, 4, 5]
Same as numpy.transpose().
Parameters:
none.
Returns:
y : array_like. An array with its axes permuted. A view is returned whenever possible.
Example:
Python
>>> import numpy
>>> a = numpy.arange(6).reshape(3, 2)
>>> a.T
array([[0, 2, 4],
[1, 3, 5]])
Javascript with PyExtJS
> a = numpy.arange(6).reshape(3, 2);
[[0, 1], [2, 3], [4, 5]]
> a.T;
[[0, 2, 4], [1, 3, 5]]
numpy.concatenate([a1, a2, ...])
Join a sequence of arrays along an existing axis.
Parameters:
a1, a2, ... : sequence of array_like. The arrays must have the same shape, except in the dimension corresponding to axis (the first, by default).
Returns:
res : ndarray. The concatenated array.
Example:
Python
>>> import numpy as np
>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> np.concatenate([a, b])
array([[1, 2],
[3, 4],
[5, 6]])
Javascript with PyExtJS
> a = np.array([[1, 2], [3, 4]]);
[[0, 1], [3, 4]]
> b = np.array([[5, 6]]);
[[5, 6]]
> np.concatenate([a, b]);
[[0, 1], [3, 4],[5, 6]]