Shuffling without repetitions:

import random

# Initial list of candidates or prizes
participants = ["Alexey", "Maria", "Ivan", "Olga", "Dmitry"]

# 1. Selecting 3 unique winners (sample without replacement)
winners = random.sample(participants, k=3)
print(f"Winners: {winners}") 
# The result is different each time, but there will be no repetitions within the list of winners!

# 2. Shuffling an entire string (creating an anagram)
word = "python"
shuffled_word = "".join(random.sample(word, len(word)))
print(f"Anagram: {shuffled_word}")

# 3. Important difference: random.choices allows repetitions
print(f"With repetitions: {random.choices(participants, k=3)}")

✨ Honest selection and generation of unique sets

When it's necessary to implement the logic of prize draws, random task distribution, or generating test questions, developers often use random.choice() in a loop. But this approach requires manually ensuring that the same element is not selected twice. The random.sample function takes on this routine.

— Guarantee of uniqueness: The main property of random.sample is "without replacement". The extracted element no longer participates in the next selection cycle, which completely eliminates duplicates in the resulting list.

— Safety of the original: The function does not modify the original list (unlike random.shuffle()), but creates a completely new array with the results. This allows the structure of the original data to remain intact.

— Strict control of size: If you pass a parameter k (the number of elements) that exceeds the length of the original list, Python will not start duplicating elements and will immediately throw an ValueError error. This protects the program logic from incorrect data.

#Python #Random #Coding #NoRepetition #DataScience #UniqueSets

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Convert PDF to structured JSON — in a couple of lines and without hassle! 📄✨

Today, we'll create a mini-service that takes a PDF document, extracts the text from it, and asks GPT to neatly organize the content into sections: title, author, date, and a list of sections. 🚀

First, let's connect the necessary libraries and API key:

import os
from PyPDF2 import PdfReader
from openai import OpenAI

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

Now, let's extract the text from the PDF. We'll loop through all the pages and combine them into a single string:

reader = PdfReader("document.pdf")
text = "
".join(page.extract_text() for page in reader.pages)

Next, we'll send the obtained text to GPT. We'll ask the model to return a structured JSON with the necessary fields:

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": (
            "You are a PDF parser. Return a JSON with the fields: title, author, date, sections. "
            "Each section is an object with name and summary."
        )},
        {"role": "user", "content": text}
    ]
)

Output the result:

structured = response.choices[0].message.content.strip()
print(structured)

🔥 Suitable for contracts, reports, methodologies, and any PDFs — we immediately get a JSON ready for use.

#PDF #JSON #Python #GPT #Automation #DataScience

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How to create your own context manager in Python for opening and closing a connection to the SQLite database

The enter() method is used when opening a connection, and the exit() method is used when closing it:

import sqlite3

class DatabaseConnection:
    def __init__(self, db_name):
        self.db_name = db_name
        self.connection = None

    def __enter__(self):
        self.connection = sqlite3.connect(self.db_name)
        return self.connection

    def __exit__(self, exc_type, exc_val, exc_tb):
        if self.connection:
            self.connection.close()

# Usage
with DatabaseConnection("example.db") as conn:
    cursor = conn.cursor()
    cursor.execute("SELECT * FROM users")

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#Python #SQLite #ContextManager #Programming #Coding #Tech

Safe rounding of numbers with math.fsum

import math

# Initial list with fractions
values = [0.1] * 10

# 1. Regular summation via sum()
print(f"Standard sum(): {sum(values)}") # 0.9999999999999999

# 2. Exact summation via math.fsum()
print(f"Exact math.fsum(): {math.fsum(values)}") # 1.0

Eliminating errors when calculating arrays

We've already discussed why float in Python loses accuracy and how Decimal deals with this. But what if you need to add a million ordinary real numbers from a database or matrix, and it's not possible to convert everything to heavy Decimal objects due to a performance hit? The math.fsum() function comes to the rescue.

— Eliminating accumulated error: When sequentially adding elements via the standard sum(), the microscopic errors of float are rounded at each step and "accumulate" in the loop. The math.fsum() function tracks intermediate accuracy losses and compensates for them during the calculations.

— High performance: Since the math module is written in C, this function works several times faster than manually iterating through the array or using alternative data types. You get the speed of basic float calculations with near-perfect accuracy.

— Stability in Data Science: This tool is indispensable when working with weights in neural networks, calculating averages of large samples, or processing financial transactions, where speed is important but it's critical not to lose valuable cents and fractions during mass operations.

🐍 #Python #DataScience #Coding #Programming #MathFsum #TechTips

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This is how the Dijkstra algorithm works.

It's a pathfinding method used to find the shortest route between nodes in a graph. 🗺️

1. Start at the source node.
2. Assign distance 0 to source, infinity to others.
3. Mark source as visited.
4. Select the unvisited node with the smallest distance.
5. Update neighbors' distances if a shorter path is found.
6. Repeat until all nodes are visited.

Key points:
- Greedy approach ✅
- No negative weights allowed ⚠️
- Time complexity: O((V + E) log V) 🕒

#Dijkstra #Algorithms #Pathfinding #ComputerScience #GraphTheory #TechEducation

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Catch a useful trick for working with division in Python 🐍

divmod() takes two numbers and in a single operation returns a tuple with the quotient and remainder from the division 📊

#Python #Coding #Programming #Tech #Tips #Dev

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Smart counting of elements using collections.Counter 📊

from collections import Counter

# Initial list with duplicate elements
logs = ["error", "info", "error", "warning", "error", "info"]

# 1. Instantly count the number of occurrences
count_dict = Counter(logs)
print(count_dict)  # Counter({'error': 3, 'info': 2, 'warning': 1})

# 2. Get the most frequent elements (Top-2)
print(count_dict.most_common(2))  # [('error', 3), ('info', 2)]

# 3. Set math for counters
clicks_day1 = Counter(item=4, banner=2)
clicks_day2 = Counter(item=1, banner=5)
# Combine the results of two days in a single operation
print(clicks_day1 + clicks_day2)  # Counter({'banner': 7, 'item': 5})

Forget about manual loops and dictionaries 🚫🔄

When you need to count the frequency of words in a text, the distribution of log types, or popular products in a store, developers usually create an empty dictionary and write a loop with a check if key not in dict: dict[key] = 1. The Counter class takes all this dirty work on itself and makes it as efficient as possible.

— Automatic initialization: You no longer need to check if a key exists in the dictionary. If the element is not there, Counter will not throw a KeyError, but simply return 0. 🛡️

— Finding leaders without sorting: The most_common(k) method returns a list of the k most frequently occurring elements. Under the hood, Python uses optimized heap algorithms, which work much faster than a full dictionary sort via sorted(). 🏆

— Mathematical operations: You can add, subtract, intersect, and merge Counter objects. This turns them into a powerful tool for aggregating metrics and analytics from different data sources in a few lines of code. ➕➖

#Python #DataScience #Coding #Programming #Automation #DevOps

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