Cheat sheet on Python Data Types 🐍

Python Data Types — the main data types in Python.

🔢 Numbers — numerical types (int, float, complex)
⚖️ Bool — logical values (True, False)
📝 String — text strings
📋 List — mutable ordered collections
🔒 Tuple — immutable ordered collections
🎲 Set — unique unordered elements
🗂️ Dict — collections of "key-value" pairs.

Helps to quickly orient yourself in data types and choose the appropriate structure for storing information 💡

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Variables in Python are stickers, not boxes.

A variable doesn't store a value, but points to it. If you change a list, both variables will see the change. This makes sense with stickers. But with boxes, it's magic: two boxes mysteriously change simultaneously.

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3 quick ways to merge dictionaries in Python 🐍

1️⃣ The operator | (Python 3.9+) — the most modern and elegant way. Creates a new dictionary.

dict1 = {'a': 1, 'b': 2}
dict2 = {'b': 99, 'c': 4}

combined = dict1 | dict2
# Result: {'a': 1, 'b': 99, 'c': 4} (values of the second dictionary replace the first)

2️⃣ The in-place update operator |= (Python 3.9+) — if you need to modify the first dictionary in place.

dict1 |= dict2

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**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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✨ 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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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.

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