10.9. Functional Map

• Map (convert) elements in sequence

• Generator (lazy evaluated)

• map(callable, *iterables)

• required callable - Function

• required iterables - 1 or many sequence or iterator objects

The map() function in Python is a built-in function that applies a given function to each element of an iterable (such as a list, tuple, or set) and returns a new iterable with the results. It takes two arguments: a function and an iterable.

The function is applied to each element in the iterable, and the results are collected into a new iterable. The resulting iterable can be converted to a list, tuple, or set if desired.

Here's an example of using the map() function to square each number in a list:

>>> data = [1, 2, 3, 4, 5]
>>>
>>> def square(n):
...     return n ** 2
>>>
>>> result = map(square, data)
>>> list(result)
[1, 4, 9, 16, 25]

In this example, the square function is applied to each element in the numbers list using the map() function. The resulting iterable contains the squared values of each element in the original list. The list() function is used to convert the iterable to a list.

>>> from inspect import isgeneratorfunction, isgenerator
>>>
>>>
>>> isgeneratorfunction(map)
False
>>>
>>> result = map(float, [1,2,3])
>>> isgenerator(result)
False

10.9.1. Example

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

>>> result = map(float, range(0,5))
>>>
>>> list(result)
[0.0, 1.0, 2.0, 3.0, 4.0]

10.9.2. Problem

>>> data = [1, 2, 3]
>>> result = []
>>>
>>> for x in data:
...     result.append(float(x))
>>>
>>> print(result)
[1.0, 2.0, 3.0]

10.9.3. Solution

>>> data = [1, 2, 3]
>>> result = map(float, data)
>>>
>>> list(result)
[1.0, 2.0, 3.0]

10.9.4. Lazy Evaluation

>>> data = [1, 2, 3]
>>> result = map(float, data)
>>>
>>> next(result)
1.0
>>> next(result)
2.0
>>> next(result)
3.0
>>> next(result)
Traceback (most recent call last):
StopIteration

10.9.5. Multi Parameters

>>> def myfunc(x):
...     return sum(x)
>>>
>>>
>>> DATA = [
...     (1,2),
...     (3,4),
... ]
>>>
>>> result = map(myfunc, DATA)
>>> print(list(result))
[3, 7]

10.9.6. Starmap

>>> from itertools import starmap
>>>
>>>
>>> DATA = [
...     (3.1415, 3),
...     (2.71828, 2)]
>>>
>>> result = starmap(round, DATA)  # round(number=3.1415, ndigits=2)
>>> print(list(result))
[3.142, 2.72]

10.9.7. Partial

>>> from functools import partial
>>>
>>>
>>> myround = partial(round, ndigits=1)
>>> DATA = [1.111, 2.222, 3.333]
>>>
>>> result = map(myround, DATA)  # round(number=1.111, ndigits=1)
>>> print(list(result))
[1.1, 2.2, 3.3]

10.9.8. Performance

>>> def even(x):
...     return x % 2 == 0

>>> 
... %%timeit -r 1000 -n 1000
... result = [float(x) for x in data if even(x)]
1.9 µs ± 206 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)

>>> 
... %%timeit -r 1000 -n 1000
... result = list(map(float, filter(parzysta, data)))
1.66 µs ± 175 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)

10.9.9. Use Case - 0x01

Built-in functions:

>>> DATA = [1, 2, 3]
>>> result = map(float, DATA)
>>>
>>> tuple(map(float, DATA))
(1.0, 2.0, 3.0)

>>> DATA = [1, 2, 3]
>>> result = map(float, DATA)
>>>
>>> set(map(float, DATA))
{1.0, 2.0, 3.0}

>>> DATA = [1, 2, 3]
>>> result = (float(x) for x in DATA)
>>>
>>> list(result)
[1.0, 2.0, 3.0]

>>> DATA = [1.1, 2.2, 3.3]
>>> result = map(round, DATA)
>>>
>>> list(result)
[1, 2, 3]

10.9.10. Use Case - 0x02

>>> def square(x):
...     return x ** 2
>>>
>>>
>>> DATA = [1, 2, 3]
>>> result = map(square, DATA)
>>>
>>> list(result)
[1, 4, 9]

10.9.11. Use Case - 0x03

>>> def increment(x):
...     return x + 1
>>>
>>>
>>> DATA = [1, 2, 3, 4]
>>> result = map(increment, DATA)
>>>
>>> list(result)
[2, 3, 4, 5]

10.9.12. Use Case - 0x04

>>> def translate(letter):
...     return PL.get(letter, letter)
>>>
>>>
>>> DATA = 'zażółć gęślą jaźń'
>>> PL = {'ą': 'a', 'ć': 'c', 'ę': 'e',
...       'ł': 'l', 'ń': 'n', 'ó': 'o',
...       'ś': 's', 'ż': 'z', 'ź': 'z'}
>>>
>>> result = map(translate, DATA)
>>> ''.join(result)
'zazolc gesla jazn'

10.9.13. Use Case - 0x05

Standard input:

>>> import sys
>>>
>>> 
... print(sum(map(int, sys.stdin)))

$ cat ~/.profile |grep addnum
alias addnum='python -c"import sys; print(sum(map(int, sys.stdin)))"'

10.9.14. Use Case - 0x06

>>> import httpx
>>>
>>> url = 'https://python3.info/_static/iris-dirty.csv'
>>>
>>> data = httpx.get(url).text
>>> header, *rows = data.splitlines()
>>> nrows, nfeatures, *class_labels = header.strip().split(',')
>>> label_encoder = dict(enumerate(class_labels))

>>> result = []
>>> for row in rows:
...     *features, species = row.strip().split(',')
...     features = map(float, features)
...     species = label_encoder[int(species)]
...     row = tuple(features) + (species,)
...     result.append(row)

>>> def decode(row):
...     *features, species = row.strip().split(',')
...     features = map(float, features)
...     species = label_encoder[int(species)]
...     return tuple(features) + (species,)
>>>
>>> result = map(decode, rows)

>>> def decode(row):
...     *features, species = row.strip().split(',')
...     features = map(float, features)
...     species = label_encoder[int(species)]
...     return tuple(features) + (species,)
>>>
>>> with open('/tmp/myfile.csv') as file:  
...     header = file.readline()
...     for line in map(decode, file):
...         print(line)

10.9.15. Use Case - 0x07

SetUp:

>>> from doctest import testmod as run_tests

Data [1]:

>>> DATA = """150,4,setosa,versicolor,virginica
... 5.1,3.5,1.4,0.2,0
... 7.0,3.2,4.7,1.4,1
... 6.3,3.3,6.0,2.5,2
... 4.9,3.0,1.4,0.2,0
... 6.4,3.2,4.5,1.5,1
... 5.8,2.7,5.1,1.9,2"""

Definition:

>>> def get_labelencoder(header: str) -> dict[int, str]:
...     """
...     >>> get_labelencoder('150,4,setosa,versicolor,virginica')
...     {0: 'setosa', 1: 'versicolor', 2: 'virginica'}
...     """
...     nrows, nfeatures, *class_labels = header.split(',')
...     return dict(enumerate(class_labels))
>>>
>>> run_tests()  
TestResults(failed=0, attempted=1)

>>> def get_data(line: str) -> tuple:
...     """
...     >>> convert('5.1,3.5,1.4,0.2,0')
...     (5.1, 3.5, 1.4, 0.2, 'setosa')
...     >>> convert('7.0,3.2,4.7,1.4,1')
...     (7.0, 3.2, 4.7, 1.4, 'versicolor')
...     >>> convert('6.3,3.3,6.0,2.5,2')
...     (6.3, 3.3, 6.0, 2.5, 'virginica')
...     """
...     *values, species = line.split(',')
...     values = map(float, values)
...     species = label_encoder[int(species)]
...     return tuple(values) + (species,)
>>>
>>> run_tests()  
TestResults(failed=0, attempted=3)

>>> header, *lines = DATA.splitlines()
>>> label_encoder = get_labelencoder(header)
>>> result = map(get_data, lines)

>>> list(result)  
[(5.1, 3.5, 1.4, 0.2, 'setosa'),
 (7.0, 3.2, 4.7, 1.4, 'versicolor'),
 (6.3, 3.3, 6.0, 2.5, 'virginica'),
 (4.9, 3.0, 1.4, 0.2, 'setosa'),
 (6.4, 3.2, 4.5, 1.5, 'versicolor'),
 (5.8, 2.7, 5.1, 1.9, 'virginica')]

10.9.16. Use Case - 0x08

>>> 
... import pandas as pd
...
...
... DATA = 'https://python3.info/_static/phones-pl.csv'
...
... result = (
...     pd
...     .read_csv(DATA, parse_dates=['datetime'])
...     .set_index('datetime', drop=True)
...     .drop(columns=['id'])
...     .loc['2000-01-01':'2000-03-01']
...     .query('item == "sms"')
...     .groupby(['period','item'])
...     .agg(
...         duration_count = ('duration', 'count'),
...         duration_sum = ('duration', 'sum'),
...         duration_median = ('duration', 'median'),
...         duration_mean = ('duration', 'mean'),
...         duration_std = ('duration', 'std'),
...         duration_var = ('duration', 'var'),
...         value = ('duration', lambda column: column.mean().astype(int))
...     )
... )

10.9.17. References

[1]

Scikit-learn Contributors. Iris Dataset. Year: 2022. Retrieved: 2022-12-19. URL: https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/sklearn/datasets/data/iris.csv

10.9.18. Assignments