3.2. Array Shape

• Any shape operation changes only np.ndarray.shape and np.ndarray.strides and does not touch data

3.2.1. SetUp

>>> import numpy as np


3.2.2. Recap

>>> obj = [1, 2, 3]
>>>
>>> len(obj)
3

>>> obj1 = [1, 2, 3]
>>> obj2 = [4, 5, 6]
>>>
>>> len([obj1, obj2])
2
>>> len([ [1,2,3], [4,5,6] ])
2
>>> len([[1,2,3],
...      [4,5,6]])
2

>>> obj1 = [1, 2, 3]
>>> obj2 = [4, 5, 6]
>>> obj3 = [7, 8, 9]
>>> obj4 = [10, 11, 12]
>>>
>>> len([ [obj1, obj2], [obj3, obj4] ])
2
>>> len([[obj1, obj2],
...      [obj3, obj4]])
2


3.2.3. Shape

1-dimensional:

>>> a = np.array([1, 2, 3])
>>> a.shape
(3,)


2-dimensional:

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>> a.shape
(2, 3)

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


3-dimensional:

>>> a = np.array([[[ 1,  2,  3],
...                [ 4,  5,  6],
...                [ 5,  6,  7]],
...               [[11, 22, 33],
...                [44, 55, 66],
...                [77, 88, 99]]])
>>> a.shape
(2, 3, 3)


3.2.4. Reshape

• Returns new array

• Does not modify inplace

• a.reshape(1, 2) is equivalent to a.reshape((1, 2))

>>> a = np.array([1, 2, 3])
>>>
>>> a.reshape(1, 3)
array([[1, 2, 3]])
>>>
>>> a.reshape(3, 1)
array([[1],
[2],
[3]])

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> a.reshape(3, 2)
array([[1, 2],
[3, 4],
[5, 6]])
>>>
>>> a.reshape(1, 6)
array([[1, 2, 3, 4, 5, 6]])
>>>
>>> a.reshape(6, 1)
array([[1],
[2],
[3],
[4],
[5],
[6]])
>>>
>>> a.reshape(5, 2)
Traceback (most recent call last):
ValueError: cannot reshape array of size 6 into shape (5,2)

>>> a = np.array([1, 2, 3, 4, 5, 6, 7, 8])
>>>
>>> a.reshape(2, 4)
array([[1, 2, 3, 4],
[5, 6, 7, 8]])
>>>
>>> a.reshape(2, 4, 1)
array([[[1],
[2],
[3],
[4]],

[[5],
[6],
[7],
[8]]])
>>>
>>> a.reshape(2, 2, 2)
array([[[1, 2],
[3, 4]],

[[5, 6],
[7, 8]]])
>>>
>>> a.reshape(1, 2, 4)
array([[[1, 2, 3, 4],
[5, 6, 7, 8]]])
>>>
>>> a.reshape(4, 2, 1)
array([[[1],
[2]],

[[3],
[4]],

[[5],
[6]],

[[7],
[8]]])
>>>
>>> a.reshape(1, 8, 1)
array([[[1],
[2],
[3],
[4],
[5],
[6],
[7],
[8]]])
>>>
>>> a.reshape(2, 3, 1)
Traceback (most recent call last):
ValueError: cannot reshape array of size 8 into shape (2,3,1)


3.2.5. Flatten

• Returns new array (makes memory copy - expensive)

• Does not modify inplace

>>> a = np.array([1, 2, 3])
>>>
>>> a.flatten()
array([1, 2, 3])

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

>>> a = np.array([[[ 1,  2,  3],
...                [ 4,  5,  6],
...                [ 5,  6,  7]],
...
...               [[11, 22, 33],
...                [44, 55, 66],
...                [77, 88, 99]]])
>>>
>>> a.flatten()
array([ 1,  2,  3,  4,  5,  6,  5,  6,  7, 11, 22, 33, 44, 55, 66, 77, 88, 99])


3.2.6. Ravel

• Ravel is the same as Flatten but returns a reference (or view) of the array if possible (i.e. memory is contiguous)

• Otherwise returns new array (makes memory copy)

>>> a = np.array([1, 2, 3])
>>>
>>> a.ravel()
array([1, 2, 3])

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

>>> a = np.array([[[ 1,  2,  3],
...                [ 4,  5,  6],
...                [ 5,  6,  7]],
...
...               [[11, 22, 33],
...                [44, 55, 66],
...                [77, 88, 99]]])
>>>
>>>
>>> a.ravel()
array([ 1,  2,  3,  4,  5,  6,  5,  6,  7, 11, 22, 33, 44, 55, 66, 77, 88, 99])


3.2.7. Flatten vs Ravel

>>> a = np.array([1, 2, 3])
>>> b = a.ravel()
>>> c = a.flatten()

>>> a[0] = 99

>>> a  # original
array([99,  2,  3])
>>>
>>> b  # flatten
array([99,  2,  3])
>>>
>>> c  # ravel
array([1, 2, 3])


3.2.9. Assignments

"""
* Assignment: Numpy Shape 1d
* Complexity: easy
* Lines of code: 2 lines
* Time: 3 min

English:
1. Define result_ravel with result of flattening DATA using .ravel() method
2. Define result_flatten with result of flattening DATA using .flatten() method
3. Define result_reshape with result of reshaping DATA into 1x9
4. Run doctests - all must succeed

Polish:
1. Zdefiniuj result_ravel z wynikiem spłaszczenia DATA używając metody .ravel()
2. Zdefiniuj result_flatten z wynikiem spłaszczenia DATA używając metody .flatten()
3. Zdefiniuj result_reshape z wynikiem zmiany kształtu DATA na 1x9
4. Uruchom doctesty - wszystkie muszą się powieść

Tests:
>>> import sys; sys.tracebacklimit = 0

>>> assert result_ravel is not Ellipsis, \
'Assign result to variable: result_ravel'
>>> assert type(result_ravel) is np.ndarray, \
'Variable result_ravel has invalid type, expected: np.ndarray'

>>> assert result_flatten is not Ellipsis, \
'Assign result to variable: result_flatten'
>>> assert type(result_flatten) is np.ndarray, \
'Variable result_flatten has invalid type, expected: np.ndarray'

>>> assert result_reshape is not Ellipsis, \
'Assign result to variable: result_reshape'
>>> assert type(result_reshape) is np.ndarray, \
'Variable result_reshape has invalid type, expected: np.ndarray'

>>> result_flatten
array([1, 2, 3, 4, 5, 6, 7, 8, 9])

>>> result_ravel
array([1, 2, 3, 4, 5, 6, 7, 8, 9])

>>> result_reshape
array([[1, 2, 3, 4, 5, 6, 7, 8, 9]])
"""

import numpy as np

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

result_ravel = ...
result_flatten = ...
result_reshape = ...