# 10.1. Loop Comprehension¶

## 10.1.1. Recap¶

>>> result = []
>>>
>>> for x in range(0,5):
...     result.append(x)
>>>
>>> print(result)
[0, 1, 2, 3, 4]


## 10.1.2. Syntax¶

result = [<RETURN> for <VARIABLE> in <ITERABLE>]


## 10.1.3. Example¶

>>> result = [x for x in range(0,5)]
>>>
>>> print(result)
[0, 1, 2, 3, 4]


## 10.1.4. Convention¶

• Use shorter variable names

• x is common name

## 10.1.5. Comprehensions and Generator Expression¶

• Comprehensions executes instantly

• Generator expression executes lazily

List Comprehension:

>>> list(x for x in range(0,5))
[0, 1, 2, 3, 4]
>>>
>>> [x for x in range(0,5)]
[0, 1, 2, 3, 4]


Set Comprehension:

>>> set(x for x in range(0,5))
{0, 1, 2, 3, 4}
>>>
>>> {x for x in range(0,5)}
{0, 1, 2, 3, 4}


Dict Comprehension:

>>> dict((x,x) for x in range(0,5))
{0: 0, 1: 1, 2: 2, 3: 3, 4: 4}
>>>
>>> {x:x for x in range(0,5)}
{0: 0, 1: 1, 2: 2, 3: 3, 4: 4}


Tuple Comprehension:

>>> tuple(x for x in range(0,5))
(0, 1, 2, 3, 4)


Generator Expression:

>>> (x for x in range(0,5))
<generator object <genexpr> at 0x...>

>>> _ = list(x for x in range(0,5))      # list comprehension
>>> _ = tuple(x for x in range(0,5))     # tuple comprehension
>>> _ = set(x for x in range(0,5))       # set comprehension
>>> _ = dict((x,x) for x in range(0,5))  # dict comprehension
>>>
>>> _ = [x for x in range(0,5)]          # list comprehension
>>> _ = (x for x in range(0,5))          # generator expression
>>> _ = {x for x in range(0,5)}          # set comprehension
>>> _ = {x:x for x in range(0,5)}        # dict comprehension


## 10.1.6. Comprehensions or Generator Expression¶

Comprehensions vs Generator Expression:

>>> data = [x for x in range(0,10)]
>>> print(data)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

>>> data = (x for x in range(0,10))
>>> print(data)
<generator object <genexpr> at 0x...>


Comprehension:

>>> data = [x for x in range(0,10)]
>>>
>>> for x in data:
...     print(x, end=' ')
...     if x == 3:
...         break
0 1 2 3
>>>
>>> for x in data:
...     print(x, end=' ')
...     if x == 6:
...         break
0 1 2 3 4 5 6
>>>
>>> print(list(data))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>>
>>> print(list(data))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]


Generator Expressions:

>>> data = (x for x in range(0,10))
>>>
>>> for x in data:
...     print(x, end=' ')
...     if x == 3:
...         break
0 1 2 3
>>>
>>> for x in data:
...     print(x, end=' ')
...     if x == 6:
...         break
4 5 6
>>>
>>> print(list(data))
[7, 8, 9]
>>>
>>> print(list(data))
[]


## 10.1.7. List Comprehension¶

>>> [x+1 for x in range(0,5)]
[1, 2, 3, 4, 5]
>>>
>>> [x-1 for x in range(0,5)]
[-1, 0, 1, 2, 3]
>>>
>>> [x**2 for x in range(0,5)]
[0, 1, 4, 9, 16]
>>>
>>> [2**x for x in range(0,5)]
[1, 2, 4, 8, 16]

>>> list(x+1 for x in range(0,5))
[1, 2, 3, 4, 5]
>>>
>>> list(x-1 for x in range(0,5))
[-1, 0, 1, 2, 3]
>>>
>>> list(x**2 for x in range(0,5))
[0, 1, 4, 9, 16]
>>>
>>> list(2**x for x in range(0,5))
[1, 2, 4, 8, 16]


## 10.1.8. Set Comprehension¶

set comprehension approach to applying function to elements:

>>> {x+10 for x in range(0, 5)}
{10, 11, 12, 13, 14}

>>> set(x+10 for x in range(0, 5))
{10, 11, 12, 13, 14}


## 10.1.9. Dict Comprehension¶

dict comprehension approach to applying function to elements:

>>> {x+10:x for x in range(0,5)}
{10: 0, 11: 1, 12: 2, 13: 3, 14: 4}

>>> dict((x+10,x) for x in range(0,5))
{10: 0, 11: 1, 12: 2, 13: 3, 14: 4}


dict comprehension approach to applying function to elements:

>>> {x:x+10 for x in range(0,5)}
{0: 10, 1: 11, 2: 12, 3: 13, 4: 14}

>>> dict((x,x+10) for x in range(0,5))
{0: 10, 1: 11, 2: 12, 3: 13, 4: 14}


dict Comprehension approach to applying function to elements:

>>> {x+10:x+10 for x in range(0,5)}
{10: 10, 11: 11, 12: 12, 13: 13, 14: 14}

>>> dict((x+10,x+10) for x in range(0,5))
{10: 10, 11: 11, 12: 12, 13: 13, 14: 14}


## 10.1.10. Tuple Comprehension?!¶

• Tuple Comprehension vs. Generator Expression

Tuple Comprehension:

>>> tuple(x+10 for x in range(0,5))
(10, 11, 12, 13, 14)


Generator Expression:

>>> (x+10 for x in range(0,5))
<generator object <genexpr> at 0x...>


## 10.1.11. Filter¶

Example 1:

>>> result = []
...
>>> for x in range(0,5):
...     if x % 2 == 0:
...         result.append(x)
>>>
>>> print(result)
[0, 2, 4]

>>> result = [x for x in range(0,5) if x%2==0]
>>> print(result)
[0, 2, 4]


Example 2:

>>> DATA = [('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),
...         (5.8, 2.7, 5.1, 1.9, 'virginica'),
...         (5.1, 3.5, 1.4, 0.2, 'setosa'),
...         (5.7, 2.8, 4.1, 1.3, 'versicolor'),
...         (6.3, 2.9, 5.6, 1.8, 'virginica'),
...         (6.4, 3.2, 4.5, 1.5, 'versicolor'),
...         (4.7, 3.2, 1.3, 0.2, 'setosa'),
...         (7.0, 3.2, 4.7, 1.4, 'versicolor')]
>>>
>>> [features for *features,label in DATA if label == 'setosa']
[[5.1, 3.5, 1.4, 0.2],
[4.7, 3.2, 1.3, 0.2]]
>>>
>>> [X for *X,y in DATA if y=='setosa']
[[5.1, 3.5, 1.4, 0.2],
[4.7, 3.2, 1.3, 0.2]]


## 10.1.12. Map¶

Applying function to each output element:

>>> [float(x) for x in range(0,5)]
[0.0, 1.0, 2.0, 3.0, 4.0]

>>> [float(x) for x in range(0,5) if x%2==0]
[0.0, 2.0, 4.0]


Applying function to each output element:

>>> [pow(2,x) for x in range(0,5)]
[1, 2, 4, 8, 16]

>>> [pow(2,x) for x in range(0,5) if x%2==0]
[1, 4, 16]


Using list comprehension for filtering:

>>> DATA = [('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),
...         (5.8, 2.7, 5.1, 1.9, 'virginica'),
...         (5.1, 3.5, 1.4, 0.2, 'setosa'),
...         (5.7, 2.8, 4.1, 1.3, 'versicolor'),
...         (6.3, 2.9, 5.6, 1.8, 'virginica'),
...         (6.4, 3.2, 4.5, 1.5, 'versicolor'),
...         (4.7, 3.2, 1.3, 0.2, 'setosa'),
...         (7.0, 3.2, 4.7, 1.4, 'versicolor')]
>>>
>>> [tuple(features) for *features,label in DATA if label == 'setosa']
[(5.1, 3.5, 1.4, 0.2),
(4.7, 3.2, 1.3, 0.2)]
>>>
>>> [tuple(X) for *X,y in DATA if y=='setosa']
[(5.1, 3.5, 1.4, 0.2),
(4.7, 3.2, 1.3, 0.2)]


## 10.1.13. Indent and Whitespaces¶

>>> result = [pow(x,2) for x in range(0,5)]
>>>
>>> result = [pow(x,2)
...           for x in range(0,5)]

>>> result = [pow(x, 2) for x in range(0, 5) if x % 2 == 0]
>>>
>>> result = [pow(x,2) for x in range(0,5) if x%2==0]

>>> result = [pow(x,2)
...           for x in range(0,5)
...               if x % 2 == 0]
>>>
>>> result = [pow(x,2)
...           for x in range(0,5)
...           if x % 2 == 0]

>>> DATA = [{'a':1, 'b':2, 'c': 3},
...         {'a':1, 'b':2, 'c': 3},
...         {'a':1, 'b':2, 'c': 3}]
>>>
>>> result = [value
...           for row in DATA
...             for key, value in row.items()]
>>>
>>> result = [value
...           for row in DATA
...           for key, value in row.items()]
>>>

>>>
... result = [astronaut | dict(addresses)
...                 if (columns := [f'{key}{i}' for key in address.keys()])
>>>
>>>
... result = [astronaut | dict(addresses)
...           if (columns := [f'{key}{i}' for key in address.keys()])


## 10.1.14. Nested¶

>>> DATA = {
...     6: ['Doctorate', 'Prof-school'],
...     5: ['Masters', 'Bachelor', 'Engineer'],
...     3: ['Junior High'],
...     2: ['Primary School'],
...     1: ['Kindergarten']}
>>>
>>> result = {}
>>> for i, titles in DATA.items():
...     for title in titles:
...         result[title] = str(i)
>>>
>>> print(result)
{'Doctorate': '6',
'Prof-school': '6',
'Masters': '5',
'Bachelor': '5',
'Engineer': '5',
'Junior High': '3',
'Primary School': '2',
'Kindergarten': '1'}
>>>
>>> print(i)
1
>>>
>>> print(title)
Kindergarten
>>>
>>> print(titles)
['Kindergarten']

>>> DATA = {
...     6: ['Doctorate', 'Prof-school'],
...     5: ['Masters', 'Bachelor', 'Engineer'],
...     3: ['Junior High'],
...     2: ['Primary School'],
...     1: ['Kindergarten']}
>>>
>>> result = {title: str(i)
...           for i, titles in DATA.items()
...           for title in titles}
>>>
>>> print(result)
{'Doctorate': '6',
'Prof-school': '6',
'Masters': '5',
'Bachelor': '5',
'Engineer': '5',
'Junior High': '3',
'Primary School': '2',
'Kindergarten': '1'}
>>>
>>>
... print(i)
Traceback (most recent call last):
NameError: name 'i' is not defined
>>>
>>>
... print(title)
Traceback (most recent call last):
NameError: name 'title' is not defined
>>>
>>>
... print(titles)
Traceback (most recent call last):
NameError: name 'titles' is not defined


## 10.1.15. Examples¶

Increment and decrement:

>>> [x+1 for x in range(0,5)]
[1, 2, 3, 4, 5]

>>> [x-1 for x in range(0,5)]
[-1, 0, 1, 2, 3]


Sum:

>>> sum(x for x in range(0,5))
10

>>> sum(x for x in range(0,5) if x%2==0)
6


Power:

>>> [pow(x,2) for x in range(0,5)]
[0, 1, 4, 9, 16]

>>> [x**2 for x in range(0,5)]
[0, 1, 4, 9, 16]

>>> [pow(2,x) for x in range(0,5)]
[1, 2, 4, 8, 16]

>>> [2**x for x in range(0,5)]
[1, 2, 4, 8, 16]


Even or Odd:

>>> [x for x in range(0,5)]
[0, 1, 2, 3, 4]

>>> [x%2==0 for x in range(0,5)]
[True, False, True, False, True]


Even or Odd:

>>> result = {}
>>>
>>> for x in range(0,5):
...     is_even = (x % 2 == 0)
...     result.update({x: is_even})
>>>
>>> print(result)
{0: True, 1: False, 2: True, 3: False, 4: True}

>>> {x: (x%2==0) for x in range(0,5)}
{0: True, 1: False, 2: True, 3: False, 4: True}


Filtering:

>>> DATA = [{'is_astronaut': True,  'name': 'Jan Twardowski'},
...         {'is_astronaut': True,  'name': 'Mark Watney'},
...         {'is_astronaut': False, 'name': 'José Jiménez'},
...         {'is_astronaut': True,  'name': 'Melissa Lewis'},
...         {'is_astronaut': False, 'name': 'Alex Vogel'}]
>>>
>>> astronauts = [person
...               for person in DATA
...               if person['is_astronaut']]
>>>
>>> print(astronauts)
[{'is_astronaut': True, 'name': 'Jan Twardowski'},
{'is_astronaut': True, 'name': 'Mark Watney'},
{'is_astronaut': True, 'name': 'Melissa Lewis'}]

>>> DATA = [{'is_astronaut': True,  'name': 'Jan Twardowski'},
...         {'is_astronaut': True,  'name': 'Mark Watney'},
...         {'is_astronaut': False, 'name': 'José Jiménez'},
...         {'is_astronaut': True,  'name': 'Melissa Lewis'},
...         {'is_astronaut': False, 'name': 'Alex Vogel'}]
>>>
>>> astronauts = [person['name']
...               for person in DATA
...               if person['is_astronaut']]
>>>
>>> print(astronauts)
['Jan Twardowski', 'Mark Watney', 'Melissa Lewis']

>>> DATA = [{'is_astronaut': True,  'name': 'Jan Twardowski'},
...         {'is_astronaut': True,  'name': 'Mark Watney'},
...         {'is_astronaut': False, 'name': 'José Jiménez'},
...         {'is_astronaut': True,  'name': 'Melissa Lewis'},
...         {'is_astronaut': False, 'name': 'Alex Vogel'}]
>>>
>>> astronauts = [{'firstname': person['name'].split()[0],
...                'lastname': person['name'].split()[1]}
...                for person in DATA
...                if person['is_astronaut']]
>>>
>>> print(astronauts)
[{'firstname': 'Jan', 'lastname': 'Twardowski'},
{'firstname': 'Mark', 'lastname': 'Watney'},
{'firstname': 'Melissa', 'lastname': 'Lewis'}]

>>> DATA = [{'is_astronaut': True,  'name': 'Jan Twardowski'},
...         {'is_astronaut': True,  'name': 'Mark Watney'},
...         {'is_astronaut': False, 'name': 'José Jiménez'},
...         {'is_astronaut': True,  'name': 'Melissa Lewis'},
...         {'is_astronaut': False, 'name': 'Alex Vogel'}]
>>>
>>> astronauts = [{'firstname': person['name'].split()[0].capitalize(),
...                'lastname': person['name'].split()[1][0]+'.'}
...                for person in DATA
...                if person['is_astronaut']]
>>>
>>> print(astronauts)
[{'firstname': 'Jan', 'lastname': 'T.'},
{'firstname': 'Mark', 'lastname': 'W.'},
{'firstname': 'Melissa', 'lastname': 'L.'}]

>>> DATA = [{'is_astronaut': True,  'name': 'Jan Twardowski'},
...         {'is_astronaut': True,  'name': 'Mark Watney'},
...         {'is_astronaut': False, 'name': 'José Jiménez'},
...         {'is_astronaut': True,  'name': 'Melissa Lewis'},
...         {'is_astronaut': False, 'name': 'Alex Vogel'}]
>>>
>>> astronauts = [{'firstname': fname, 'lastname': lname}
...                for person in DATA
...                if person['is_astronaut']
...                and (name := person['name'].split())
...                and (fname := name[0].capitalize())
...                and (lname := f'{name[1][0]}.')]
>>>
>>> print(astronauts)
[{'firstname': 'Jan', 'lastname': 'T.'},
{'firstname': 'Mark', 'lastname': 'W.'},
{'firstname': 'Melissa', 'lastname': 'L.'}]

>>> DATA = [{'is_astronaut': True,  'name': 'Jan Twardowski'},
...         {'is_astronaut': True,  'name': 'Mark Watney'},
...         {'is_astronaut': False, 'name': 'José Jiménez'},
...         {'is_astronaut': True,  'name': 'Melissa Lewis'},
...         {'is_astronaut': False, 'name': 'Alex Vogel'}]
>>>
>>> astronauts = [f'{fname} {lname[0]}.'
...               for person in DATA
...               if person['is_astronaut']
...               and (fullname := person['name'].split())
...               and (fname := fullname[0].capitalize())
...               and (lname := fullname[1].upper())]
>>>
>>> print(astronauts)
['Jan T.', 'Mark W.', 'Melissa L.']


Using list comprehension for filtering with more complex expression:

>>> DATA = [('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),
...         (5.8, 2.7, 5.1, 1.9, 'virginica'),
...         (5.1, 3.5, 1.4, 0.2, 'setosa'),
...         (5.7, 2.8, 4.1, 1.3, 'versicolor'),
...         (6.3, 2.9, 5.6, 1.8, 'virginica'),
...         (6.4, 3.2, 4.5, 1.5, 'versicolor'),
...         (4.7, 3.2, 1.3, 0.2, 'setosa'),
...         (7.0, 3.2, 4.7, 1.4, 'versicolor')]
>>>
>>>
>>> def is_setosa(species):
...     if species == 'setosa':
...         return True
...     else:
...         return False
>>>
>>>
>>> [tuple(X) for *X,y in DATA if is_setosa(y)]
[(5.1, 3.5, 1.4, 0.2),
(4.7, 3.2, 1.3, 0.2)]


Quick parsing lines:

>>> DATA = ['5.8,2.7,5.1,1.9,virginica',
...         '5.1,3.5,1.4,0.2,setosa',
...         '5.7,2.8,4.1,1.3,versicolor']
>>>
>>> result = []
>>>
>>> for row in DATA:
...     row = row.split(',')
...     result.append(row)
>>>
>>> print(result)
[['5.8', '2.7', '5.1', '1.9', 'virginica'],
['5.1', '3.5', '1.4', '0.2', 'setosa'],
['5.7', '2.8', '4.1', '1.3', 'versicolor']]
>>>
>>> [row.split(',') for row in DATA]
[['5.8', '2.7', '5.1', '1.9', 'virginica'],
['5.1', '3.5', '1.4', '0.2', 'setosa'],
['5.7', '2.8', '4.1', '1.3', 'versicolor']]


Reversing dict keys with values:

>>> DATA = {'a': 1, 'b': 2}
>>>
>>> list(DATA.items())
[('a', 1),
('b', 2)]
>>> [(k,v) for k,v in DATA.items()]
[('a', 1),
('b', 2)]
>>> [(v,k) for k,v in DATA.items()]
[(1, 'a'),
(2, 'b')]
>>>
>>> {v:k for k,v in DATA.items()}
{1: 'a', 2: 'b'}


Value collision while reversing dict:

>>> DATA = {'a': 1, 'b': 2, 'c': 2}
>>>
>>> {v:k for k,v in DATA.items()}
{1: 'a', 2: 'c'}


## 10.1.16. All and Any¶

>>> all(x for x in range(0,5))
False
>>> any(x for x in range(0,5))
True

>>> DATA = [{'is_astronaut': True,  'name': 'Jan Twardowski'},
...         {'is_astronaut': True,  'name': 'Mark Watney'},
...         {'is_astronaut': False, 'name': 'José Jiménez'},
...         {'is_astronaut': True,  'name': 'Melissa Lewis'},
...         {'is_astronaut': False, 'name': 'Alex Vogel'}]
>>>
>>> if all(person['is_astronaut'] for person in DATA):
...     print('Everyone is astronaut')
... else:
...     print('Not everyone is astronaut')
Not everyone is astronaut

>>> DATA = [{'is_astronaut': True,  'name': 'Jan Twardowski'},
...         {'is_astronaut': True,  'name': 'Mark Watney'},
...         {'is_astronaut': False, 'name': 'José Jiménez'},
...         {'is_astronaut': True,  'name': 'Melissa Lewis'},
...         {'is_astronaut': False, 'name': 'Alex Vogel'}]
>>>
>>> if any(person['is_astronaut'] for person in DATA):
...     print('At least one person is astronaut')
... else:
...     print('There are no astronauts')
At least one person is astronaut

>>> DATA = [('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),
...         (5.8, 2.7, 5.1, 1.9, 'virginica'),
...         (5.1, 3.5, 1.4, 0.2, 'setosa'),
...         (5.7, 2.8, 4.1, 1.3, 'versicolor'),
...         (6.3, 2.9, 5.6, 1.8, 'virginica'),
...         (6.4, 3.2, 4.5, 1.5, 'versicolor'),
...         (4.7, 3.2, 1.3, 0.2, 'setosa'),
...         (7.0, 3.2, 4.7, 1.4, 'versicolor')]
>>>
>>> all(observation > 1.0
...     for *features, label in DATA[1:]
...     for observation in features
...     if isinstance(observation, float))
False
>>>
>>> all(x > 1.0
...     for *X,y in DATA[1:]
...     for x in X
...     if isinstance(x, float))
False


## 10.1.17. Conditional Expression¶

>>> result = ['even' if x % 2 == 0 else 'odd'
...           for x in range(0,10)]
>>> result
['even', 'odd', 'even', 'odd', 'even', 'odd', 'even', 'odd', 'even', 'odd']

>>> result = ['even' if x % 2 == 0 else 'odd'
...           for x in range(0,10)
...           if x % 3 == 0]
>>> result
['even', 'odd', 'even', 'odd']


## 10.1.18. Assignments¶

"""
* Assignment: Loop Comprehension Create
* Required: yes
* Complexity: easy
* Lines of code: 2 lines
* Time: 3 min

English:
1. Use list comprehension
2. Generate result: list[int]
of even numbers from 5 to 20 (without 20)
3. Run doctests - all must succeed

Polish:
1. Użyj rozwinięcia listowego
2. Wygeneruj result: list[int]
parzystych liczb z przedziału 5 do 20 (bez 20)
3. Uruchom doctesty - wszystkie muszą się powieść

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

>>> assert type(result) is list, \
'Result should be a list'

>>> assert all(type(x) is int for x in result), \
'Result should be a list of int'

>>> result
[6, 8, 10, 12, 14, 16, 18]
"""

# list[int]: even numbers from 5 to 20 (without 20)
result = ...


"""
* Assignment: Loop Comprehension Months
* Required: yes
* Complexity: easy
* Lines of code: 1 lines
* Time: 3 min

English:
1. Use dict comprehension and enumerate
2. Convert MONTH into dict:
a. Keys: month number
b. Values: month name
3. Run doctests - all must succeed

Polish:
1. Użyj rozwinięcia słownikowego i enumeracji
2. Przekonwertuj MONTH w słownik:
a. klucz: numer miesiąca
b. wartość: nazwa miesiąca
3. Uruchom doctesty - wszystkie muszą się powieść

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

>>> type(result)
<class 'dict'>
>>> 0 not in result
True
>>> 13 not in result
True
>>> result[1] == 'January'
True

>>> assert all(type(x) is int for x in result.keys())
>>> assert all(type(x) is str for x in result.values())

>>> result  # doctest: +NORMALIZE_WHITESPACE
{1: 'January',
2: 'February',
3: 'March',
4: 'April',
5: 'May',
6: 'June',
7: 'July',
8: 'August',
9: 'September',
10: 'October',
11: 'November',
12: 'December'}
"""

MONTHS = ['January', 'February', 'March', 'April',
'May', 'June', 'July', 'August', 'September',
'October', 'November', 'December']

# dict[str,str]: number and month name
result = ...


"""
* Required: yes
* Complexity: easy
* Lines of code: 1 lines
* Time: 3 min

English:
1. Use dict comprehension and enumerate
2. Convert MONTH into result: dict[str,str]:
a. Keys: month number
b. Values: month name
3. Month number must be two letter string
(zero padded) - f'{number:02}'
4. Run doctests - all must succeed

Polish:
1. Użyj rozwinięcia słownikowego i enumeracji
2. Przekonwertuj MONTH w result: dict[str,str]:
a. klucz: numer miesiąca
b. wartość: nazwa miesiąca
3. Numer miesiąca ma być dwuznakowym stringiem
(wypełnij zerem) - f'{number:02}'
4. Uruchom doctesty - wszystkie muszą się powieść

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

>>> type(result)
<class 'dict'>
>>> '00' not in result
True
>>> '13' not in result
True
>>> result['01'] == 'January'
True

>>> assert all(type(x) is str for x in result.keys())
>>> assert all(type(x) is str for x in result.values())
>>> assert all(len(x) == 2 for x in result.keys())

>>> result  # doctest: +NORMALIZE_WHITESPACE
{'01': 'January',
'02': 'February',
'03': 'March',
'04': 'April',
'05': 'May',
'06': 'June',
'07': 'July',
'08': 'August',
'09': 'September',
'10': 'October',
'11': 'November',
'12': 'December'}
"""

MONTHS = ['January', 'February', 'March', 'April',
'May', 'June', 'July', 'August', 'September',
'October', 'November', 'December']

# dict[str,str]: with zero-padded number and month name
result = ...


"""
* Assignment: Loop Comprehension Translate
* Required: yes
* Complexity: easy
* Lines of code: 1 lines
* Time: 3 min

English:
1. Use list comprehension to iterate over DATA
2. If letter is in PL then use conversion value as letter
3. Add letter to result
4. Run doctests - all must succeed

Polish:
1. Użyj rozwinięcia listowego do iteracji po DATA
2. Jeżeli litera jest w PL to użyj skonwertowanej wartości jako litera
3. Dodaj literę do result
4. Uruchom doctesty - wszystkie muszą się powieść

Hint:
* use str.join()

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

>>> assert type(result) is str

>>> result
'zazolc gesla jazn'
"""

PL = {'ą': 'a', 'ć': 'c', 'ę': 'e',
'ł': 'l', 'ń': 'n', 'ó': 'o',
'ś': 's', 'ż': 'z', 'ź': 'z'}

DATA = 'zażółć gęślą jaźń'

# str: DATA with substituted PL diacritic chars to ASCII letters
result = ...


"""
* Assignment: Loop Comprehension Split
* Required: no
* Complexity: medium
* Lines of code: 9 lines
* Time: 13 min

English:
1. Using List Comprehension split DATA into:
a. features_train: list[tuple] - 60% of first features in DATA
b. features_test: list[tuple] - 40% of last features in DATA
c. labels_train: list[str] - 60% of first labels in DATA
d. labels_test: list[str] - 40% of last labels in DATA
2. In order to do so, calculate pivot point:
a. length of DATA times given percent (60% = 0.6)
b. remember, that slice indicies must be int, not float
c. for example: if dataset has 10 rows, then 6 rows will be for
training, and 4 rows for test
3. Run doctests - all must succeed

Polish:
1. Używając List Comprehension podziel DATA na:
a. features_train: list[tuple] - 60% pierwszych features w DATA
b. features_test: list[tuple] - 40% ostatnich features w DATA
c. labels_train: list[str] - 60% pierwszych labels w DATA
d. labels_test: list[str] - 40% ostatnich labels w DATA
2. Aby to zrobić, wylicz punkt podziału:
a. długość DATA razy zadany procent (60% = 0.6)
b. pamiętaj, że indeksy slice muszą być int a nie float
c. na przykład: if zbiór danych ma 10 wierszy, to 6 wierszy będzie
do treningu, a 4 do testów
3. Uruchom doctesty - wszystkie muszą się powieść

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

>>> assert type(features_train) is list, \
'make sure features_train is a list'

>>> assert type(features_test) is list, \
'make sure features_test is a list'

>>> assert type(labels_train) is list, \
'make sure labels_train is a list'

>>> assert type(labels_test) is list, \
'make sure labels_test is a list'

>>> assert all(type(x) is tuple for x in features_train), \
'all elements in features_train should be tuple'

>>> assert all(type(x) is tuple for x in features_test), \
'all elements in features_test should be tuple'

>>> assert all(type(x) is str for x in labels_train), \
'all elements in labels_train should be str'

>>> assert all(type(x) is str for x in labels_test), \
'all elements in labels_test should be str'

>>> features_train  # doctest: +NORMALIZE_WHITESPACE
[(5.8, 2.7, 5.1, 1.9),
(5.1, 3.5, 1.4, 0.2),
(5.7, 2.8, 4.1, 1.3),
(6.3, 2.9, 5.6, 1.8),
(6.4, 3.2, 4.5, 1.5),
(4.7, 3.2, 1.3, 0.2)]

>>> features_test  # doctest: +NORMALIZE_WHITESPACE
[(7.0, 3.2, 4.7, 1.4),
(7.6, 3.0, 6.6, 2.1),
(4.9, 3.0, 1.4, 0.2),
(4.9, 2.5, 4.5, 1.7)]

>>> labels_train
['virginica', 'setosa', 'versicolor', 'virginica', 'versicolor', 'setosa']

>>> labels_test
['versicolor', 'virginica', 'setosa', 'virginica']
"""

DATA = [('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),
(5.8, 2.7, 5.1, 1.9, 'virginica'),
(5.1, 3.5, 1.4, 0.2, 'setosa'),
(5.7, 2.8, 4.1, 1.3, 'versicolor'),
(6.3, 2.9, 5.6, 1.8, 'virginica'),
(6.4, 3.2, 4.5, 1.5, 'versicolor'),
(4.7, 3.2, 1.3, 0.2, 'setosa'),
(7.0, 3.2, 4.7, 1.4, 'versicolor'),
(7.6, 3.0, 6.6, 2.1, 'virginica'),
(4.9, 3.0, 1.4, 0.2, 'setosa'),
(4.9, 2.5, 4.5, 1.7, 'virginica')]

ratio = 0.6