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A comprehensive Python training course that systematically covers the basics of the language: variables, conditions, loops, functions, lists, dictionaries, OOP, etc. The material is accompanied by code examples and clear explanations, making it suitable even for those who are just starting their journey in programming.
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
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👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
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My favorite way to work with multiple filters in pandas.Series — not a chain of .loc, but a single mask. 🐼
The chain looks neat, but breaks on real data and easily gives unexpected results:
The problem is that the second .loc again looks at the original s, not the already filtered result. The logic gets messy. 🤯
It's more reliable to gather everything into one expression:
One mask, one point of truth. ✅
It's easier to debug. Fewer surprises when the code grows. 🚀
#Pandas #Python #DataScience #CodingTips #DataEngineering #Debugging
The chain looks neat, but breaks on real data and easily gives unexpected results:
s = pd.Series([10, 15, 20, 25, 30])
s.loc[s > 20].loc[s % 2 == 1]
The problem is that the second .loc again looks at the original s, not the already filtered result. The logic gets messy. 🤯
It's more reliable to gather everything into one expression:
s = pd.Series([10, 15, 20, 25, 30])
mask = (s > 20) & (s % 2 == 1)
result = s.loc[mask]
One mask, one point of truth. ✅
It's easier to debug. Fewer surprises when the code grows. 🚀
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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:
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import cupy as cp
The same code can run on CUDA up to 100 times faster ⚡️.
What it can do:
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→ No need to rewrite the program or learn new syntax 📝.
→ Supports not only CUDA but also AMD ROCm 💻.
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Forwarded from Machine Learning
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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:
Performance Benchmarks demonstrate speed improvements in various use cases. 📈
More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing
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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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