# 2.3. Array Shape¶

## 2.3.1. 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


## 2.3.2. Rationale¶

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

## 2.3.3. Shape¶

import numpy as np

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

a.shape
# (3,)

import numpy as np

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

a.shape
# (2, 3)

import numpy as np

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

a.shape
# (3, 3)

import numpy as np

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)


## 2.3.4. Reshape¶

• Returns new array

• Does not modify inplace

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

import numpy as np

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

a.reshape(1, 3)
# array([[1, 2, 3]])

a.reshape(3, 1)
# array([[1],
#        [2],
#        [3]])

import numpy as np

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)

import numpy as np

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)


## 2.3.5. Flatten¶

• Returns new array (makes memory copy - expensive)

• Does not modify inplace

import numpy as np

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

a.flatten()
# array([1, 2, 3])

import numpy as np

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

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

import numpy as np

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])


## 2.3.6. 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)

import numpy as np

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

a.ravel()
# array([1, 2, 3])

import numpy as np

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

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

import numpy as np

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])


## 2.3.8. 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

>>> type(result_ravel) is np.ndarray
True
>>> type(result_flatten) is np.ndarray
True
>>> type(result_reshape) is np.ndarray
True
>>> 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 = ...