Here's a small fact about Python 🐍

The := operator is called the "walrus" because the symbols resemble the eyes and tusks of a walrus 🦭

It was introduced in Python 3.8 and allows you to assign a value to a variable and use it directly within the expression at the same time.

For example:

while (line := input("Say something: ")) != "quit":
    print(f"You said: {line}")

Without it, you would have to retrieve the value separately using input(), and then check it.

Have you ever used the := operator in your code?

#Python #Programming #WalrusOperator #Coding #TechFacts #Python3

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Do not violate the Single Responsibility Principle 🎯

A function should do one thing, and do it well.

This function does too much:

def calculate_final_total(
    price: float,
    quantity: int,
    discount_rate: float,
    tax_rate: float
) -> float:
    # Calculate the subtotal
    subtotal = price * quantity

    # Apply the discount
    discounted_amount = subtotal * (1 - discount_rate)

    # Calculate the tax
    final_total = discounted_amount * (1 + tax_rate)

    return final_total

The problem here is that the calculation of the subtotal, discount, and tax are all combined into one function. Any change to one of these steps can affect the entire calculation.

It's better to break down the logic into smaller, more specialized functions:

def calculate_subtotal(price: float, quantity: int) -> float:
    return price * quantity

def apply_discount(subtotal: float, discount: float) -> float:
    return subtotal * (1 - discount)

def calculate_tax(amount: float, tax_rate: float) -> float:
    return amount * (1 + tax_rate)

This is much better. ✅

Smaller functions with a single task are easier to test with unit tests because they have fewer dependencies and require less mocking.

Furthermore, isolated components are easier to reuse in different parts of the application or pipeline without bringing in unnecessary dependencies.

Therefore, keep your functions simple and focused.

One function – one responsibility. 📝

#Python #Coding #SoftwareDevelopment #CleanCode #Programming #BestPractices

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🌍 From Python Practice to Real Projects

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The tell() function in Python 🐍

The tell() function returns the current position of the file pointer within the data stream. It is most often used when working with files. 📂

The function does not accept any arguments and returns an integer – the position in bytes from the beginning of the stream. 🔢

with open("file.txt", "rb") as f:
    print(f.tell())

#Python #Programming #Coding #FileHandling #DevTips #Tech

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📌 Marking Deprecated Code in Python: deprecated()

In large projects, old functions are not always immediately removed. They are often left for compatibility, but it's important to warn developers that they should no longer be used.

Previously,

warnings.warn()

was often used for this purpose:

import warnings

def old_api():
    warnings.warn(
        "Use new_api()",
        DeprecationWarning
    )

In Python 3.13, a

deprecated()

decorator has been introduced:

from warnings import deprecated

@deprecated("Use new_api()")
def old_api():
    return "old"

Now, the information about deprecation is part of the API itself. It can be recognized not only during runtime, but also by editors and static analysis tools.

This also works for classes:

@deprecated("Use NewClient")
class OldClient:
    pass

This is convenient for libraries, SDKs, and large projects where the API is gradually changing.

#Python #Python313 #Deprecated #Coding #Programming #SoftwareDevelopment

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👩‍💻 heapq.merge(): Combining sorted data!

If you have multiple sources of data that are already sorted, you don't need to collect them into a single collection and sort them again. heapq.merge() combines such sources into a single, ordered iterator.

In this guide:
• We will combine multiple sorted sequences;
• We will explore lazy processing of large data sources;
• We will configure comparison using the key argument;
• We will combine data sorted in reverse order.

This is especially useful when working with logs, files, and query results, where each source already provides data in the correct order.

#Python #heapq #DataProcessing #CodingTips #Programming #Algorithms

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Using pickletools.dis() to analyze serialized data.

🐍 pickle doesn't just store a snapshot of an object; it stores a sequence of instructions for its subsequent reconstruction.

Normally, we only see the result of serialization:

import pickle

payload = pickle.dumps(
    {"name": "Alex", "roles": ["admin", "user"]}
)

print(len(payload))

The standard library includes pickletools.dis(), which disassembles the pickle stream and shows its instructions in a readable format.

import pickletools

pickletools.dis(payload)

The output shows the creation of a dictionary, strings, a list, and the operations used to assemble the final object.

EMPTY_DICT
SHORT_BINUNICODE 'name'
SHORT_BINUNICODE 'Alex'
SHORT_BINUNICODE 'roles'
EMPTY_LIST

This is useful when debugging your own classes: you can check which global objects and reconstruction mechanisms are included in the serialization.

class User:
    def init(self, name):
        self.name = name

payload = pickle.dumps(User("Alex"))

pickletools.dis(payload)

For further analysis, there's pickletools.optimize(): it removes some unused operations from the pickle stream without changing the object being reconstructed.

optimized = pickletools.optimize(payload)

assert pickle.loads(optimized).name == "Alex"

⚠️ pickletools is designed for analyzing pickle streams, not for safely reading untrusted data. You should still not pass unknown pickle data to pickle.loads().

🔥 pickletools.dis() allows you to peek inside the serialization and see the instructions from which pickle reconstructs the object.

#Python #Pickle #DataScience #Debugging #Coding #DevTools

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