A Chinese developer has released an open-source replacement for NumPy that performs calculations on GPUs. It's called CuPy 🚀. In many cases, it's enough to replace a single line:

import cupy as cp

The same code can run on CUDA up to 100 times faster ⚡️.

What it can do:
→ Compatible with existing NumPy and SciPy code 🛠️.
→ No need to rewrite the program or learn new syntax 📝.
→ Supports not only CUDA but also AMD ROCm 💻.

The project is completely open-source 📂:
🔗 https://github.com/cupy/cupy

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Python Programming for Beginners: Learn Python from Scratch

Python Programming for Beginners: Learn Python from Scratch (Master Data Analysis, Step-by-Step with Practice Exercises)…

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🔖 A useful training tool for Data Scientists 📊

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A Powerful Alternative to Pandas 🚀

This is an optimized replacement for Pandas that can significantly speed up data processing without requiring major changes to your code. ⚙️

To get started, simply replace a single import:

import fireducks.pandas as pd

Performance Benchmarks demonstrate speed improvements in various use cases. 📈

More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing

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Here's a small fact about Python 🐍

The := operator is called the "walrus" because the symbols resemble the eyes and tusks of a walrus 🦭

It was introduced in Python 3.8 and allows you to assign a value to a variable and use it directly within the expression at the same time.

For example:

while (line := input("Say something: ")) != "quit":
    print(f"You said: {line}")

Without it, you would have to retrieve the value separately using input(), and then check it.

Have you ever used the := operator in your code?

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