Learn Python Coding

📉 The bitcoin is falling, boss! We will teach Python to monitor the cryptocurrency rate and notify if the rate is above or below the threshold. We will connect the requests library and import time:

import requests
import time

We will create a function to get the BTC price in USD via the CoinGecko API:

def get_btc_price():
    url = "https://api.coingecko.com/api/v3/simple/price?ids=bitcoin&vs_currencies=usd"
    r = requests.get(url)
    return r.json()["bitcoin"]["usd"]

Now — the main monitoring cycle. We will set a threshold and check the price every minute:

threshold = 65000  # specify your goal
while True:
    price = get_btc_price()
    print(f"BTC: ${price}")
    if price > threshold:
        print("🚀 Time to sell!")
        break
    time.sleep(60)

🔥 You can also easily adapt it for Ethereum, DOGE, or even Telegram Token — just replace bitcoin with the desired coin in the URL. 🚪 @DataScience4

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This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

✨ poetry-core | Python Tools ✨ 📖 A lightweight build backend for Python. 🏷️ #Python

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I rarely say this, but this is the best repository for mastering Python. The course is led by David Beazley, the author of Python Cookbook (3rd edition, O'Reilly) and Python Distilled (Addison-Wesley). In this PythonMastery.pdf, all the information is structured 👾 Link: https://github.com/dabeaz-course/python-mastery/blob/main/PythonMastery.pdf In the Exercises folder, all the exercises are located 👾 Link: https://github.com/dabeaz-course/python-mastery/tree/main/Exercises In the Solutions folder — the solutions 👾 Link: https://github.com/dabeaz-course/python-mastery/tree/main/Solutions 👉 @codeprogrammer

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✨ isort | Python Tools ✨ 📖 A command-line utility and library for sorting and organizing Python imports. 🏷️ #Python

✨ mypy | Python Tools ✨ 📖 A static type checker for Python. 🏷️ #Python

Tip: Efficiently Slice Iterators and Large Sequences with itertools.islice Explanation: Traditional list slicing (my_list[start:end]) creates a new list in memory containing the sliced elements. While convenient for small lists, this becomes memory-inefficient for very large lists and is impossible for pure iterators (like generators or file objects) that don't support direct indexing. itertools.islice provides a memory-optimized solution by returning an iterator that yields elements from a source iterable (list, generator, file, etc.) between specified start, stop (exclusive), and step indices, without first materializing the entire slice into a new collection. This "lazy" consumption of the source iterable is crucial for processing massive datasets, infinite sequences, or streams where only a portion is needed, preventing excessive memory usage and improving performance. It behaves syntactically similar to standard slicing but operates at the iterator level. Example:

import itertools
import sys

# A generator for a very large sequence
def generate_large_sequence(count):
    for i in range(count):
        yield f"Data_Item_{i}"

# Imagine needing to process only a small segment of 10 million items
total_items = 10**7
data_stream = generate_large_sequence(total_items)

# Get items from index 500 to 509 (inclusive)
# Using islice:
print("--- Using itertools.islice ---")
# islice(iterable, [start], stop, [step])
# Here, start=500, stop=510 (exclusive)
for item in itertools.islice(data_stream, 500, 510):
    print(item)

# Compare memory usage (conceptual, as actual list materialization would be massive)
# If you tried:
# large_list = list(generate_large_sequence(total_items)) # <-- HUGE memory consumption here!
# for item in large_list[500:510]:
#    print(item)

# islice consumes minimal memory, only holding iterator state.
# The `data_stream` generator itself only holds its current state, not the whole sequence.
print("\n`itertools.islice` memory footprint is negligible compared to creating a full list slice.")

no any words from your ━━━━━━━━━━━━━━━ By: @DataScience4 ✨

✨ Quiz: Writing DataFrame-Agnostic Python Code With Narwhals ✨ 📖 If you're a Python library developer wondering how to write DataFrame-agnostic code, the Narwhals library is the solution you're looking for. 🏷️ #advanced #data-science #python

✨ Pylint | Python Tools ✨ 📖 A static code checker for Python. 🏷️ #Python

✨ pytest | Python Tools ✨ 📖 A test runner and framework for Python. 🏷️ #Python

✨ flake8 | Python Tools ✨ 📖 A command-line Python linter. 🏷️ #Python

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✨ Git | Python Tools ✨ 📖 A distributed version control system. 🏷️ #Python