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Python: Uncovering the Ignored Core Functionalities | by Erik van de Ven | Jul, 2023

Python: Uncovering the Ignored Core Functionalities | by Erik van de Ven | Jul, 2023

2023-07-24 15:06:26

Photograph by Stefan Steinbauer on Unsplash

What Kyle Simpson mentions about JavaScript in his books, Luciano Ramalho mentions in his ebook ‘Fluent Python’ about Python. They mainly deal with the identical subject of each languages. To place it in my very own phrases:

As a result of the language is very easy to be taught, many practitioners solely scratch the floor of its full potential, neglecting to delve into the extra superior and highly effective facets of the language which makes it so really distinctive and highly effective

So let’s focus on briefly all functionalities you would possibly haven’t heard of, however you positively wish to know should you goal to turn into a very seasoned Pythonista.

Python arguments are evaluated when the perform definition is encountered. Good to keep in mind that! Which means that every time the fib_memo perform (which is talked about under) known as with out explicitly offering a price for the memo argument, it should use the identical dictionary object that was created when the perform was outlined.

def fib_memo(n, memo={0:0, 1:1}):
n is the quantity nth quantity
you wish to return within the sequence
if not n in memo:
memo[n] = fib_memo(n-1) + fib_memo(n-2)
return memo[n]

# sixth Fibonacci (together with 0 as first quantity)
fib_memo(6) # ought to return 8

So this code works, in Python. This additionally signifies that you could possibly execute the fib_memo perform in a single script a number of occasions, like in a for loop, with every execution growing the fibonacci quantity to be computed, with out hitting the “most recursion depth exceeded” restrict, because the memo will maintain increasing. More information can be found in my other article.

The walrus operator (:= ), launched in Python 3.8, means that you can assign a price to a variable inside an expression. This manner, you possibly can assign the worth to a variable and verify its worth in a single expression:

import random
some_value = random.randint(0,100) # return a quantity between 0 and, together with, 100

if((below_ten := some_value) < 10):
print(f"{below_ten} is smaller than 10")

Clearly, it’s additionally simple to assign and verify whether or not the returned worth incorporates a truthy worth or not:

if(outcome := some_method()): # If outcome just isn't Falsy

With the asterisk (* ) you possibly can unpack the arguments or key phrase arguments (with ** ) earlier than passing them to the perform. For instance, let’s contemplate the next code:

my_numbers = [1,2]

def sum_numbers(first_number, second_number):
return first_number + second_number

# This can return a TypeError.
# TypeError: sum() lacking 1 required positional argument: 'second_number'

# This can return the anticipated outcome, 3

After we name the sum_numbers perform with out unpacking my_numbers, it raises a TypeError as a result of the perform expects two separate arguments. Nevertheless, through the use of the asterisk (*), we are able to unpack the values from my_numbers and move them as particular person arguments, ensuing within the right output.

This unpacking approach works not solely with tuples and lists, but in addition with dictionaries (although it should move the keys as arguments). However what about key phrase arguments? For that, we are able to make the most of the double asterisk (**). Take the next code for example:

def greet_person(last_name, first_name):
print(f"Howdy {first_name} {last_name}")

knowledge = {"first_name": "John", "last_name": "Doe"}

Apart from unpacking a sequence to move them as arguments to a perform, you could possibly additionally use it to create a brand new sequence, for instance:

numbers = [1, 2, 3, 4, 5]
new_list_numbers = [*numbers]

The unique numbers record stays unaffected, and you’ve got a new_list_numbers variable which incorporates a duplicate of the identical record. Watch out with hyperlinks containing objects, although:

numbers = [[1, 2], [3, 4], [5, 6]]
packed_numbers = [*numbers]

numbers[0].append(10) # Modify the nested record inside the unique record

print(numbers) # Output: [[1, 2, 10], [3, 4], [5, 6]]
print(packed_numbers) # Output: [[1, 2, 10], [3, 4], [5, 6]]

any and all are built-in features that function on iterable objects (equivalent to lists, tuples, or units) and return a Boolean worth based mostly on the weather within the iterable. An instance:

some_booleans = [True, False, False, False]

any(some_booleans) # returns True
all(some_booleans) # returns

You would use the all and any features together with record comprehensions, which return an iterable and move it as argument to the all features:

numbers = [5, 10, 3, 8, -2]
all_positive = all(num > 0 for num in numbers)

… or any features:

fruits = ['apple', 'banana', 'cherry', 'durian']

# Verify if all fruits begin with 'a'
outcome = all(fruit.startswith('a') for fruit in fruits)
print(outcome) # Output: False

A desk which reveals the variations of outputs relying on the values within the iterable, is proven under.

You may mix tuple packing (what is going on on the correct of the equal (=) signal) and unpacking (what is going on on the left of the equal(=) signal) and leverage this performance for swapping variables:

a = 10
b = 5

# Swap the values of b and a by packing and unpacking
a, b = b, a

print(a) # 5
print(b) # 10

We’re used to changing some variable or worth to a string, utilizingstr(some_value), so we are able to print it for debugging functions. I wish to make you conscious of repr(some_value). The primary distinction is that repr tries to return a printable illustration of the thing, whereas str simply tries to return a string illustration. A greater instance is proven under:

import datetime

right this moment =

print(str(right this moment)) # Output: 2023-07-20 15:30:00.123456
print(repr(right this moment)) # Output: datetime.datetime(2023, 7, 20, 15, 30, 0, 123456)

As you possibly can see, str() merely returns the datetime as a string illustration. If you wish to decide whether or not the variable right this moment incorporates a string or a datetime object, you wouldn’t be capable to discern that info from this alone. Then again, ⁣repr() supplies details about the precise object that the variable holds. This info is considerably extra helpful throughout debugging.

We will maintain this straightforward: if you need to get the primary and final worth of a sequence in a single command:

first, *center, final = [1, 2, 3, 4, 5]

print(first) # 1
print(center) # [2, 3, 4]
print(final) # 5

However this works as properly

*the_first_three, second_last, final = [1, 2, 3, 4, 5]

print(the_first_three) # [1, 2, 3]
print(second_last) # 4
print(final) # 5

See Also

Or different combos.

We’re used to utilizing one context supervisor at a time, like opening a file:

with open('file.txt', 'r') as file:
# Code that makes use of the file
# The file will likely be routinely closed on the finish of the block
# even when an exception happens

# Instance: studying traces from the file
for line in file:

with open('file_2.txt', 'r') as other_file:
# Second context supervisor

for line in other_file:

However we may simply open a number of information in a single assertion. Straightforward if you need to write down traces to the opposite file for instance:

with open('file1.txt') as file1, open('file2.txt') as file2:
# Code that makes use of each file1 and file2
# The information will likely be routinely closed on the finish of the block
# even when an exception happens

# Instance: studying traces from file1 and writing them to file2
for line in file1:

We may simply print a ton of variables in our file, for debugging functions, or we may merely use the Python Debugger (pdb), which helps us to set breakpoints which makes it a lot simpler:

import pdb

# Set this breakpoint someplace in your code

What makes this a lot extra helpful is that this system will cease on the breakpoint at which you could possibly print any variable to verify its worth or existence at that particular breakpoint. Strive it! These are a number of instructions you could possibly use when this system hits a breakpoint:

  • n or subsequent: Execute the subsequent line.
  • s or step: Step right into a perform name.
  • c or proceed: Proceed execution till the subsequent breakpoint.
  • l or record: Present the present code context.
  • p <expression> or pp <expression>: Print the worth of an expression.
  • b <line> or break <line>: Set a brand new breakpoint on the specified line.
  • h or assist: Get assistance on utilizing pdb.
  • q or give up: Stop the debugger and terminate this system.

The Counter class from the collections module supplies a handy technique to rely components in an iterable:

from collections import Counter

my_list = [1, 2, 3, 1, 2, 1, 3, 4, 5]
counts = Counter(my_list)
print(counts) # Output: Counter({1: 3, 2: 2, 3: 2, 4: 1, 5: 1})

We may mix completely different for loops to create permutations, combos or a cartesian product, or we may merely use the in-built itertools.


import itertools

# Producing permutations
perms = itertools.permutations([1, 2, 3], 2)
print(record(perms)) # Output: [(1, 2), (1, 3), (2, 1), (2, 3), (3, 1), (3, 2)]


import itertools

# Producing combos
combs = itertools.combos('ABC', 2)
print(record(combs)) # Output: [('A', 'B'), ('A', 'C'), ('B', 'C')]

Cartesian product

import itertools

# Producing Cartesian product
cartesian = itertools.product('AB', [1, 2])
print(record(cartesian)) # Output: [('A', 1), ('A', 2), ('B', 1), ('B', 2)]

These are two methods to make use of the underscore in Python: as a separater for giant numbers or as a throwaway variable.

Throwaway Variable

The underscore _ can be utilized as a throwaway variable to discard undesirable values:

# Ignoring the primary return worth of a perform
_, outcome = some_function()

# Looping with out utilizing the loop variable
for _ in vary(5):

# You simply want the primary and the final
first, _, final = [1, 2, 3, 4, 5]

Separater for Massive Numbers

You need to use underscores (_) as visible separators to boost readability when working with massive numeric values. This characteristic was launched in Python 3.6 and is called “underscore literals.”

inhabitants = 7_900_000_000
income = 3_249_576_382.50

print(inhabitants) # Output: 7900000000
print(income) # Output: 3249576382.5

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