# 7.6. Comprehensions

## 7.6.1. Loop Information Recap

Listing 122. Iterative approach to applying function to elements
output = []

for x in range(0,5):
output.append(x+10)

print(output)
# [10, 11, 12, 13, 14]


## 7.6.2. Comprehensions Syntax

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

[x for x in (0,1,2,3,4)]
# [0, 1, 2, 3, 4]

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

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


## 7.6.3. Generator expressions vs. Comprehensions

• Comprehensions executes instantly

• Generator expression executes lazily

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(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((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(x for x in range(0,5))       # (0, 1, 2, 3, 4)
(x for x in range(0,5))            # <generator object <genexpr> at 0x118c1aed0>

Listing 123. Comprehension
data = [x for x in range(0,10)]

for x in data:
print(x)
if x == 3:
break

# 0
# 1
# 2
# 3

for x in data:
print(x)
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]

Listing 124. Generator
data = (x for x in range(0,10))

for x in data:
print(x)
if x == 3:
break

# 0
# 1
# 2
# 3

for x in data:
print(x)
if x == 6:
break

# 4
# 5
# 6

print(list(data))
# [7, 8, 9]

print(list(data))
# []


## 7.6.4. Simple usage

### 7.6.4.1. List Comprehension

Listing 125. list Comprehension approach to applying function to elements
[x+10 for x in range(0, 5)]
# [10, 11, 12, 13, 14]

list(x+10 for x in range(0,5))
# [10, 11, 12, 13, 14]


### 7.6.4.2. Set Comprehension

Listing 126. 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}


### 7.6.4.3. Dict Comprehension

Listing 127. 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}

Listing 128. 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}

Listing 129. 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}


### 7.6.4.4. Tuple Comprehension?!

• Tuple Comprehension vs. Generator Expression

• More in chapter Generators

Listing 130. Tuple Comprehension
tuple(x for x in range(0,5))
# (0, 1, 2, 3, 4)

Listing 131. Generator Expression
(x+10 for x in range(0, 5))
# <generator object <genexpr> at 0x11eaef570>


## 7.6.5. Conditional Comprehension

Listing 132. Iterative approach to applying function to selected elements
output = []

for x in range(0, 5):
if x % 2 == 0:
output.append(x)

print(output)
# [0, 2, 4]

Listing 133. list Comprehensions approach to applying function to selected elements
[x for x in range(0, 5) if x % 2 == 0]
# [0, 2, 4]


### 7.6.5.1. Filtering dict items

DATA = [
{'first_name': 'Иван', 'last_name': 'Иванович', 'agency': 'Roscosmos'},
{'first_name': 'Jose', 'last_name': 'Jimenez', 'agency': 'NASA'},
{'first_name': 'Melissa', 'last_name': 'Lewis', 'agency': 'NASA'},
{'first_name': 'Alex', 'last_name': 'Vogel', 'agency': 'ESA'},
{'first_name': 'Mark', 'last_name': 'Watney', 'agency': 'NASA'},
]

astronauts = [astro for astro in DATA if astro['agency'] == 'NASA']
print(astronauts)
# [{'first_name': 'Jose', 'last_name': 'Jimenez', 'agency': 'NASA'},
#  {'first_name': 'Melissa', 'last_name': 'Lewis', 'agency': 'NASA'},
#  {'first_name': 'Mark', 'last_name': 'Watney', 'agency': 'NASA'}]

astronauts = [astro['last_name'] for astro in DATA if astro['agency'] == 'NASA']
print(astronauts)
# ['Jimenez', 'Lewis', 'Watney']

astronauts = [(astro['first_name'], astro['last_name'])
for astro in DATA
if astro['agency'] == 'NASA']
print(astronauts)
# [
#   ('Jose', 'Jimenez'),
#   ('Melissa', 'Lewis'),
#   ('Mark', 'Watney')
# ]


## 7.6.6. Applying function

Listing 134. 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]

Listing 135. 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]

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


## 7.6.7. Examples

### 7.6.7.1. Sum

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

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


### 7.6.7.2. Filtering results

Listing 136. Using list comprehension for result 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'),
]

[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],
# ]

[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],
# ]

[f for *f,l in DATA if l == '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],
# ]


### 7.6.7.3. Filtering with complex expressions

Listing 137. Using list comprehension for result 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

[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],
# ]


### 7.6.7.4. Quick parsing lines

Listing 138. 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',
]

output = []

for row in DATA:
row = row.split(',')
output.append(row)

print(output)
# [
#   ['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']
# ]

Listing 139. 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',
]

output = [row.split(',') for row in DATA]

print(output)
# [
#   ['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']
# ]


### 7.6.7.5. Reversing dict keys with values

Listing 140. 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'}

Listing 141. Value collision while reversing dict
DATA = {'a': 1, 'b': 2, 'c': 2}

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


### 7.6.7.6. Nested

INPUT = {
6: ['Doctorate', 'Prof-school'],
5: ['Masters', 'Bachelor', 'Engineer'],
3: ['Junior High'],
2: ['Primary School'],
1: ['Kindergarten'],
}

output = {education: str(key)
for key, names in INPUT.items()
for education in names}

print(output)
# {
#   'Doctorate': '6',
#   'Prof-school': '6',
#   'Masters': '5',
#   'Bachelor': '5',
#   'Engineer': '5',
#   'Junior High': '3',
#   'Primary School': '2',
#   'Kindergarten': '1'
# }


## 7.6.8. Assignments

### 7.6.8.1. Comprehensions

• Complexity level: medium

• Lines of code to write: 8 lines

• Estimated time of completion: 15 min

English
1. For given data structure INPUT: List[tuple] (see below)

2. Separate header from data

3. Calculate pivot point: length of data times given percent

4. Using List Comprehension split data to:

• features: List[tuple] - list of measurements (each measurement row is a tuple)

• labels: List[str] - list of species names

5. Split those data structures with proportion:

• features_train: List[tuple] - features to train - 60%

• features_test: List[tuple] - features to test - 40%

• labels_train: List[str] - labels to train - 60%

• labels_test: List[str] - labels to test - 40%

6. Create output: Tuple[list, list, list, list] with features (training and test) and labels (training and test)

7. Print output

8. Compare results with "Output" section below

Polish
1. Dana jest struktura danych INPUT: List[tuple] (patrz sekcja input)

2. Odseparuj nagłówek od danych

3. Wylicz punkt podziału: długość danych razy zadany procent

4. Używając List Comprehension podziel dane na:

• features: List[tuple] - lista pomiarów (każdy wiersz z pomiarami ma być tuple)

• labels: List[str] - lista nazw gatunków

5. Podziel te struktury danych w proporcji:

• features_train: List[tuple] - features do uczenia - 60%

• features_test: List[tuple] - features do testów - 40%

• labels_train: List[str] - labels do uczenia - 60%

• labels_test: List[str] - labels do testów - 40%

6. Stwórz output: Tuple[list, list, list, list] z cechami (treningowymi i testowymi) oraz labelkami (treningowymi i testowymi)

7. Wypisz output

8. Porównaj wynik z sekcją "Output" poniżej

Input
INPUT = [
('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'),
(7.1, 3.0, 5.9, 2.1, 'virginica'),
(4.6, 3.4, 1.4, 0.3, 'setosa'),
(5.4, 3.9, 1.7, 0.4, 'setosa'),
(5.7, 2.8, 4.5, 1.3, 'versicolor'),
(5.0, 3.6, 1.4, 0.3, 'setosa'),
(5.5, 2.3, 4.0, 1.3, 'versicolor'),
(6.5, 3.0, 5.8, 2.2, 'virginica'),
(6.5, 2.8, 4.6, 1.5, 'versicolor'),
(6.3, 3.3, 6.0, 2.5, 'virginica'),
(6.9, 3.1, 4.9, 1.5, 'versicolor'),
(4.6, 3.1, 1.5, 0.2, 'setosa'),
]

Output
from typing import List, Dict

features_train: List[tuple]
# [(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),
#  (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), (7.1, 3.0, 5.9, 2.1), (4.6, 3.4, 1.4, 0.3)]

features_test: List[tuple]
# [(5.4, 3.9, 1.7, 0.4), (5.7, 2.8, 4.5, 1.3), (5.0, 3.6, 1.4, 0.3),
#  (5.5, 2.3, 4.0, 1.3), (6.5, 3.0, 5.8, 2.2), (6.5, 2.8, 4.6, 1.5),
#  (6.3, 3.3, 6.0, 2.5), (6.9, 3.1, 4.9, 1.5), (4.6, 3.1, 1.5, 0.2)]

labels_train: List[str]
# ['virginica', 'setosa', 'versicolor', 'virginica', 'versicolor',
#  'setosa', 'versicolor', 'virginica', 'setosa', 'virginica',
#  'virginica', 'setosa']

labels_test: List[str]
# ['setosa', 'versicolor', 'setosa', 'versicolor', 'virginica',
#  'versicolor', 'virginica', 'versicolor', 'setosa']

output: Tuple[list, list, list, list]
# ([(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),
#   (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), (7.1, 3.0, 5.9, 2.1), (4.6, 3.4, 1.4, 0.3)],
#
#  [(5.4, 3.9, 1.7, 0.4), (5.7, 2.8, 4.5, 1.3), (5.0, 3.6, 1.4, 0.3),
#   (5.5, 2.3, 4.0, 1.3), (6.5, 3.0, 5.8, 2.2), (6.5, 2.8, 4.6, 1.5),
#   (6.3, 3.3, 6.0, 2.5), (6.9, 3.1, 4.9, 1.5), (4.6, 3.1, 1.5, 0.2)],
#
#  ['virginica', 'setosa', 'versicolor', 'virginica', 'versicolor',
#   'setosa', 'versicolor', 'virginica', 'setosa', 'virginica',
#   'virginica', 'setosa'],
#
#  ['setosa', 'versicolor', 'setosa', 'versicolor', 'virginica',
#   'versicolor', 'virginica', 'versicolor', 'setosa'])

The whys and wherefores
• Iterating over nested data structures

• Using slices

• Type casting

• List comprehension

• Magic Number