9.9. Comprehension Nested¶

9.9.1. Syntax¶

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

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

9.9.2. Example¶

>>> DATA = {
...     6: ['Doctorate', 'Prof-school'],
...     5: ['Masters', 'Bachelor', 'Engineer'],
...     4: ['HS-grad'],
...     3: ['Junior High'],
...     2: ['Primary School'],
...     1: ['Kindergarten']}
>>>
>>>
>>> result = {}
>>> for lvl, titles in DATA.items():
...     for title in titles:
...         result[title] = lvl
>>>
>>> print(result)  
{'Doctorate': 6,
 'Prof-school': 6,
 'Masters': 5,
 'Bachelor': 5,
 'Engineer': 5,
 'HS-grad': 4,
 'Junior High': 3,
 'Primary School': 2,
 'Kindergarten': 1}

>>> DATA = {
...     6: ['Doctorate', 'Prof-school'],
...     5: ['Masters', 'Bachelor', 'Engineer'],
...     4: ['HS-grad'],
...     3: ['Junior High'],
...     2: ['Primary School'],
...     1: ['Kindergarten']}
>>>
>>>
>>> result = {title: lvl
...           for lvl, titles in DATA.items()
...           for title in titles}
>>>
>>> print(result)  
{'Doctorate': 6,
 'Prof-school': 6,
 'Masters': 5,
 'Bachelor': 5,
 'Engineer': 5,
 'HS-grad': 4,
 'Junior High': 3,
 'Primary School': 2,
 'Kindergarten': 1}

9.9.3. Microbenchmark¶

>>> DATA = {
...     6: ['Doctorate', 'Prof-school'],
...     5: ['Masters', 'Bachelor', 'Engineer'],
...     4: ['HS-grad'],
...     3: ['Junior High'],
...     2: ['Primary School'],
...     1: ['Kindergarten'],
... }

>>> # %%timeit -r 1000 -n 1000
>>> result = {title: lvl
...           for lvl, titles in DATA.items()
...           for title in titles}
>>> # 2.22 µs ± 138 ns per loop (mean ± std. dev. of 1000 runs, 1000 loops each)

>>> # %%timeit -r 1000 -n 1000
>>> result = {t:l for l,ts in DATA.items() for t in ts}
>>> # 2.22 µs ± 181 ns per loop (mean ± std. dev. of 1000 runs, 1000 loops each)

>>> # %%timeit -r 1000 -n 1000
>>> result = {}
>>> for lvl, titles in DATA.items():
...     for title in titles:
...         result[title] = lvl
>>> # 2.24 µs ± 152 ns per loop (mean ± std. dev. of 1000 runs, 1000 loops each)

9.9.4. Nested¶

>>> 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')]
>>>
>>>
>>> result = '\n'.join(','.join(str(x) for x in row) for row in DATA)
>>>
>>> print(result)
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

9.9.5. Hybrid Solution¶

>>> 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')]
>>>
>>>
>>> data = []
>>>
>>> for row in DATA:
...     line = ','.join(str(x) for x in row)
...     data.append(line)
>>>
>>> result = '\n'.join(data)
>>>
>>> print(result)
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

9.9.6. Code Readability¶

>>> 
... result = [astronaut | dict(addresses)
...           for astronaut in json.loads(DATA)
...             for i, address in enumerate(astronaut.pop('addresses'), start=1)
...                 if (columns := [f'{key}{i}' for key in address.keys()])
...                     and (addresses := zip(columns, address.values()))]

>>> 
... result = [astronaut | dict(addresses)
...           for astronaut in json.loads(DATA)
...           for i, address in enumerate(astronaut.pop('addresses'), start=1)
...           if (columns := [f'{key}{i}' for key in address.keys()])
...           and (addresses := zip(columns, address.values()))]

9.9.7. Assignments¶

Code 9.8. Solution¶

"""
* Assignment: Comprehension Nested Dict
* Required: yes
* Complexity: easy
* Lines of code: 1 lines
* Time: 5 min

English:
    1. Convert to `result: dict[str, str]`
    2. Use nested dict comprehension
    3. Run doctests - all must succeed

Polish:
    1. Przekonwertuj do `result: dict[str, str]`
    2. Użyj zagnieżdżonego rozwinięcia słownikowego
    3. Uruchom doctesty - wszystkie muszą się powieść

Hints:
    * nested `for`
    * `dict.items()`
    * `str()`

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

    >>> type(result)
    <class 'dict'>

    >>> result  # doctest: +NORMALIZE_WHITESPACE
    {'Doctorate': 6,
     'Prof-school': 6,
     'Masters': 5,
     'Bachelor': 5,
     'Engineer': 5,
     'HS-grad': 4,
     'Junior High': 3,
     'Primary School': 2,
     'Kindergarten': 1}
"""

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

# Converted DATA. Note values are str not int!
# type: dict[str,str]
result = ...