# 2.11. Array Sort¶

## 2.11.1. Sort¶

import numpy as np

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

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

import numpy as np

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

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

b.sort(axis=0)
b
# array([[2, 3, 1],
#        [5, 6, 4],
#        [9, 7, 8]])

c.sort(axis=1)
c
# array([[7, 8, 9],
#        [1, 2, 3],
#        [4, 5, 6]])


## 2.11.2. Flip¶

• Does not modify inplace

• Returns new np.ndarray

• Reverse the order of elements in an array along the given axis

import numpy as np

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

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

import numpy as np

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

a.flip()
# array([[6, 5, 4],
#        [3, 2, 1]])

np.flip(a, axis=0)
# array([[4, 5, 6],
#        [1, 2, 3]])

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


## 2.11.3. Assignments¶

"""
* Assignment: Numpy Sort
* Complexity: easy
* Lines of code: 2 lines
* Time: 3 min

English:
1. Define result_sort with sorted DATA by columns
2. Define result_flip with flipped DATA by rows
3. Run doctests - all must succeed

Polish:
1. Zdefiniuj result_sort z posortowanym DATA po kolumnach
2. Zdefiniuj result_flip z flipniętym DATA po wierszach
3. Uruchom doctesty - wszystkie muszą się powieść

Hints:
* .sort() returns None

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

>>> type(result_sort) is np.ndarray
True
>>> type(result_flip) is np.ndarray
True
>>> result_sort
array([[44, 47, 64, 67],
[ 9, 21, 67, 83],
[36, 70, 87, 88]])
>>> result_flip
array([[36, 70, 87, 88],
[ 9, 21, 67, 83],
[44, 47, 64, 67]])
"""

import numpy as np

DATA = np.array([[44, 47, 64, 67],
[67,  9, 83, 21],
[36, 87, 70, 88]])

result_sort = ...
result_flip = ...