Outliers can significantly skew statistical analysis and machine learning model performance. This guide covers every practical method to detect, visualize, and handle outliers in Python — from IQR and Z-Score to Isolation Forest — with runnable code at each step.
NumPy’s reshape() and flatten() are both used for array manipulation, but they serve different purposes and have distinct behaviors. This guide explains when and how to use each method effectively.
TL;DR
reshape() returns a view (no copy) when possible — memory-efficient, changes affect original. flatten() always returns a copy — safe to modify independently. Use ravel() instead of flatten() when you want a view (like reshape(-1)) to save memory. Use reshape(-1) to flatten without copying; use flatten() only when you need an independent 1D copy. What is reshape() in NumPy # The reshape() method changes the shape of an array without changing its data. It returns a new view of the array with a different shape when possible.