**Today we will examine __call__ — a data filter object!** 🧠

It allows a class instance to work as a function, preserving the state and filtering rules. ⚙️

Let's create a filter for numbers that only passes even ones and strictly greater than a specified threshold:

class EvenFilter:
    def __init__(self, threshold):
        self.threshold = threshold

    def __call__(self, numbers):
        return [n for n in numbers if n % 2 == 0 and n > self.threshold]

Let's use the filter in practice:

f = EvenFilter(5)
nums = [1, 4, 6, 7, 10]
print(f(nums))  # [6, 10]

Now each instance can have its own rules:

f2 = EvenFilter(8)
print(f2(nums))  # [10]

🔥 So, __call__ turns an object into a "smart function" with memory and customizable logic. 💡

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collections.Counter — counting elements in a single line. 📊

Counting elements without loops with Counter 🔄

Do you need to count how many times each word appears in a text or how many duplicates there are in a list? Don't reinvent the wheel with for loops and dictionaries. The built-in collections module will do everything for you. 🚀

🛠 Code:

from collections import Counter

words = ["apple", "banana", "apple", "cherry", "banana", "apple"]
word_counts = Counter(words)

print(word_counts)
# Output: Counter({'apple': 3, 'banana': 2, 'cherry': 1})

# Bonus: the top 2 most frequent elements
print(word_counts.most_common(2))
# Output: [('apple', 3), ('banana', 2)]

Ideal for basic data analysis and solving tasks on LeetCode. 💻

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Search for a substring in Python 🐍

In this example, two simple ways of finding a substring in a string are shown, which allow to solve the task without unnecessary code 💻

# Example implementation
def find_substring(text, sub):
    return text.find(sub)

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📌 How to make code cleaner with any() and all() 🐍

Do you often have to check lists for compliance with conditions? Forget about cumbersome loops! 🚫🔄

any() — returns True if at least one element is true. ✅
all() — returns True only if all elements are true. 🔒

# Example: checking if there are negative numbers
numbers = [1, 5, -3, 7]

# Bad: through a loop

has_negative = False
for num in numbers:
    if num < 0:
        has_negative = True

# Beautiful:

has_negative = any(num < 0 for num in numbers) # True ✨

What's the difference between is and == in Python?

The == operator checks whether the values of two objects are equal. In contrast, is determines whether variables refer to same object in memory. That is, == compares the content, while is checks the identity of the objects 🐍🔍

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🚀 Looking for a portfolio-ready NLP project?

I recently published an end-to-end walkthrough on Towards Data Science using Kaggle’s Spooky Author Identification dataset.

You’ll see how far classical NLP can go with:

📝 Bag-of-Words and TF-IDF
🔤 Character n-grams
📊 Model comparison
🧩 Ensemble stacking

It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step:

🔗 https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/

💡 Replacing if-else with Match-Case

Starting with Python 3.10, we have a powerful tool: Structural Pattern Matching (match-case). This is not just an analog of switch-case from other languages; it's much more flexible. 🚀

Imagine you're writing a command handler for a bot. 🤖

❌ How NOT to do it:

def handle_command(command):
    if command == "start":
        return "Hello! I'm a bot."
    elif command == "help":
        return "Here's a list of available commands..."
    elif command == "stop":
        return "Goodbye!"
    else:
        return "Unknown command."

⚡ How to do it properly:

def handle_command(command):
    match command:
        case "start":
            return "Hello! I'm a bot."
        case "help":
            return "Here's a list of available commands..."
        case "stop":
            return "Goodbye!"
        case _:  # The underscore symbol catches everything else (default)
            return "Unknown command."

The code looks like a clear table, and your eye doesn't get caught up in a bunch of elif statements. 🧐
You can pass data structures in the case statements and check their structure and content on the fly. 🔍
It's easy to combine cases. 🧩

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✨ Unpacking the remaining elements 🧩

Sometimes you need to extract the first and last elements from a list, while grouping everything in the middle separately. Instead of struggling with slicing ([1:-1]), use the asterisk (*). ⭐️

data = ["CEO", "Middle Python Dev", "Junior Dev", "QA", "HR"]

# The asterisk automatically collects everything "extra" into a separate list.
boss, *team, hr = data

print(boss)    # CEO
print(team)    # ['Middle Python Dev', 'Junior Dev', 'QA']
print(hr)      # HR

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Cheat sheet on Python Frameworks:

Django: A full-featured web framework with built-in ORM, admin panel, and security features.

Flask: A lightweight microframework with a minimal set of features and high flexibility.

ORM & Admin: Built-in to Django, but need to be connected separately in Flask.

Security: Django has built-in security mechanisms, while in Flask, they need to be configured manually.

Testing: Django offers built-in testing tools, while Flask relies on third-party libraries.

Use Cases: Django is suitable for large and complex projects, while Flask is better for small applications, APIs, and prototypes.

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