Python Tips

Posted on Wednesday, Jul 01, 2020 in programming

A collection of small Python scripts and tips.

This post is based on a Twitter thread I started in April 2020 and works as a centralized way to read all the tips in an easier format than Twitter's 280 characters.

Both will be updated frequently.

List Flatten without explicit loops

Using Itertools' chain

import itertools

test = [[-1, -2], [30, 40], [25, 35]]
list(itertools.chain.from_iterable(test))

>>  [-1, -2, 30, 40, 25, 35]

Using map and strings (Suggested by @Romestantc )

test =  [[-1, -2], [30, 40], [25, 35]]
map(int, ''.join(c for c in test.__str__() if c not in '[]').split(',') )

>> [-1, -2, 30, 40, 25, 35]

Not pretty in my opinion ;)

Count individual items of any iterable

from collections import Counter
count = Counter(['a', 'b', 'c', 'a', 'a', 'b', 'd'])

print(count)
>> Counter({'a': 3, 'b': 2, 'c': 1, 'd': 1})

count['a']
>> 3

Repeat a series of values from any iterable

import itertools as it

data = it.cycle([1, 2])

for i in range(10):
    print(next(data))

>> 1
2
1
2
1
2
1
2
1
2

Name slices to reuse them

# slice(start, end, step)

STEPTWO = slice(None, None, 2)
integer_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

integer_list[STEPTWO]

>> [0, 2, 4, 6, 8]

This is the same as:

1	integer_list[::2]

Reverse any "indexable" collection that supports slices

# slice(None, None, -1) or [::-1]

integer_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
integer_list[::-1]

>> [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]

Use array to keep homogeneous type of objects in your lists

from array import array

integer_list = array('i', [1, 2, 3])

integer_list = array('i', [1, 2, "a"])
>> TypeError: an integer is required (got type str)

The constructor of array includes a type code parameter and an optional initial list.

Swap dictionary key-values using zip

Using zip:

data = { 'a': 1, 'b': 2, 'c': 3, 'd': 4 }

data.items()
>> dict_items([('a', 1), ('b', 2), ('c', 3), ('d', 4)])

z = zip(data.values(), data.keys())

dict(z)
>>  { 1: 'a', 2: 'b', 3: 'c', 4: 'd' }

Using dictionary comprehensions:

data = { 'a': 1, 'b': 2, 'c': 3, 'd': 4 }

{ v:k for k, v in data.items() }
>>  { 1: 'a', 2: 'b', 3: 'c', 4: 'd' }

If there are repeated values, the result will be overwritten, so be careful.

Namedtuples as lightweight, inmutable, record-like objects

from collections import namedtuple

Person = namedtuple("Person", "name age gender")
bob = Person("Bob", 30, "male")

bob.age
>> 30

bob[1]
>> 30

Something similar can be achieved using Dataclasses (for Python 3.8+)

Built-ins set and frozenset for unordered collections of hashable objects

A = set([1, 2, 3])
B = set([3, 4, 5])

A.union(B)
>> {1, 2, 3, 4, 5}

A.intersection(B)
>> {3}

A.symmetric_difference(B)
>> {1, 2, 4, 5}

frozenset has the same interface but returns an inmutable set object.

Itertools' dropwhile and takewhile to filter from an iterator

from itertools import  dropwhile

numbers = [-2, -1, 0, 1, 2]

f = lambda x: x < 1

list ( dropwhile (f, numbers) )
>>  [1, 2]

takewhile takes the items if True. The difference with built-in function filter is that the iteration stops whenever the test-function is false.

numbers = [-2, -1, 0, 1, 2, -3, -4]
list(dropwhile(f, numbers))

>> [1, 2, -3, -4]

As @jgomo3 Observes, one way to think about those functions is that they simply divide a sequence in halves.

takewhile gives you the first part. dropwhile the last part.

Traspose a matrix with List Comprehensions

M = [[1,2,3],
     [4,5,6],
     [7,8,9]]

MT = [[row[i] for row in M] for i in range(len(M))]

print(MT)
>> [[1, 4, 7], [2, 5, 8],  [3, 6, 9]]

Also, NumPy provides methods to for easier matrix manipulation.

Datetime to UTC

To create timezone aware datetimes in Python:

from datetime import datetime, timezone, timedelta

tz = timezone(timedelta(hours=-6))  # UTC-6
local = datetime(2020, 4, 16, 13, 40, 0, 0, tzinfo=tz)

local.isoformat()
>> '2020-04-16T13:40:00-06:00'

from datetime import datetime, timezone

# assuming `local` is a datetime object

print(local.isoformat() )
>> '2020-04-16T13:40:00-06:00'

local_in_utc = local.astimezone(timezone.utc)

print(local_in_utc.isoformat())
>> '2020-04-16T19:40:00+00:00'

You can always use pytz to handle timezones.

from datetime import datetime
from pytz import timezone

dt = datetime(2020, 4, 16, 13, 40, 0, tzinfo=pytz.utc)

print(dt.isoformat())
>> '2020-04-16T13:40:00+00:00'

functools.singledispatch to achieve parametric polymorphism in Python

from functools import singledispatch


@singledispatch
def process(num=None):
    raise NotImplementedError("Implement process function.")

@process.register(int)
def sub_process(num):
    # processing interger
    return f"Integer {num} has been processed successfully!"

@process.register(float)
def sub_process(num):
    # processing float
    return f"Float {num} has been processed successfully!"

# use the function
print(process(12.0))
print(process(1))