Part 5: Specialized Web Scraping – Social Media, Mobile Apps, Dark Web, and Advanced Data Extraction

Duration: ~60 minutes

Link A: https://hackmd.io/@husseinsheikho/WS-5A

Link B: https://hackmd.io/@husseinsheikho/WS-5B

#SocialMediaScraping #MobileScraping #DarkWeb #FinancialData #MediaExtraction #AuthScraping #ScrapingSaaS #APIReverseEngineering #EthicalScraping #DataScience

✨ How to Drop Null Values in pandas ✨

📖 Learn how to use .dropna() to drop null values from pandas DataFrames so you can clean missing data and keep your Python analysis accurate.

🏷️ #basics #datascience #python

✨ Polars vs pandas: What's the Difference? ✨

📖 Discover the key differences in Polars vs pandas to help you choose the right Python library for faster, more efficient data analysis.

🏷️ #intermediate #datascience #python

The Python library itertools contains many useful functions. 🐍✨

One of them is compress(), which returns an iterator over the elements from data, for which the corresponding element in selectors is equal to True. 🔍💻

Here's an example: 📝👇

#Python #Programming #Itertools #Coding #Tech #DataScience

Cheat sheet on the basics of Python: 🐍📚

basic syntax and language rules 📝
scalar types — basic data types (int, float, bool, str, NoneType) 🔢

datetime — working with date and time 📅⏰

data structures — Python data structures (list, tuple, dict, set) 🗄

list — mutable lists for storing data collections 📋
tuple — immutable sequences of values 🔒
dict (hash map) — storing data in a key-value format 🗝
set — unique elements without order 🔘

slicing — obtaining parts of sequences through indices and step ✂️

module/library — connecting modules and libraries 🔌

help functions — using help() and dir() to explore the Python API 🛠

#Python #Coding #DataScience #Programming #Tech #DevCommunity

⚡️ How Redis counts billions of unique values while barely using memory

There's an algorithm called HyperLogLog. It allows you to roughly estimate how many unique elements have passed through the system, using about 12 KB of memory.

The idea is simple: Redis doesn't store the elements themselves.

It does the following:

- Takes an element
- Calculates a hash from it
- Uses part of the hash as a cell number
- Checks the other part to see how many consecutive zeros it contains
- If the new number is larger than the old one, it updates the cell

Why does this work?

Because a long series of zeros in the hash is rare.

For example:

- 1 consecutive zero - quite common
- 5 consecutive zeros - less common
- 10 consecutive zeros - about a 1 in 1024 chance
- 20 consecutive zeros - a very rare event

If Redis sees a very rare pattern, it means that many different elements have likely passed through it.

Redis uses 16,384 small counters. Each stores the maximum "rarity" it has seen for its group of elements.

Then Redis combines these values mathematically to get an estimate of unique elements.

Not an exact number, but a very close approximation.

The main trick of HyperLogLog:

it can handle millions or even billions of values, but memory hardly increases at all.

That's why Redis can count unique users, IPs, requests, or events without huge tables and lists.

#Redis #HyperLogLog #DataScience #Tech #BigData #MemoryEfficiency

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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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