vamseeachanta 1. Use Vectorization (+3)
1. Use Vectorization
# ❌ Bad: Loop
result = np.zeros(len(x))
for i in range(len(x)):
result[i] = x[i]**2 + y[i]**2
# ✅ Good: Vectorized
result = x**2 + y**2
2. Avoid Unnecessary Copies
# ❌ Bad: Creates copies
a = np.array([1, 2, 3])
b = a
b[0] = 10 # Modifies original
# ✅ Good: Explicit copy when needed
a = np.array([1, 2, 3])
b = a.copy()
b[0] = 10 # Original unchanged
3. Use In-Place Operations
# ❌ Bad: Creates new array
a = a + 1
# ✅ Good: In-place
a += 1
4. Choose Appropriate Data Types
# Use float32 for large arrays when precision allows
large_array = np.zeros((10000, 10000), dtype=np.float32) # 400 MB instead of 800 MB