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. 🚀
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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. 🚀
#Pandas #Python #DataScience #CodingTips #DataEngineering #Debugging
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Forwarded from Machine Learning
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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🔗 https://github.com/cupy/cupy
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