Chapter 2: NumPy & Data Wrangling

NumPy is the foundation of scientific computing in Python. Every image processing, machine learning, and data science library builds on NumPy arrays. Whether you're processing images pixel by pixel, training neural networks, or analyzing tabular datasets, NumPy arrays are at the core.

Why NumPy matters:

1. Universal data structure: Images are 2D/3D arrays, feature matrices are 2D arrays, neural network weights are multi-dimensional arrays
2. Vectorized operations: Process millions of elements without explicit loops -- 10-100x faster than pure Python
3. Memory efficiency: Contiguous storage, typed arrays, views instead of copies
4. Interoperability: OpenCV, PyTorch, TensorFlow, scikit-learn all use NumPy-compatible arrays

Pandas builds on NumPy to provide labeled, tabular data structures. A DataFrame is like a spreadsheet in Python: rows represent observations, columns represent features, and you can refer to data by name rather than position. Pandas excels at loading data from various sources, handling missing values, merging datasets, and computing aggregations.

Real-world data is messy. You'll encounter missing values, outliers, inconsistent formats, and features on vastly different scales. Data preprocessing often takes 80% of a data scientist's time, and this chapter teaches the essential techniques.

What you'll learn:

1. Array Creation & Operations -- Building arrays, understanding shape/dtype, vectorized computation
2. Indexing & Slicing -- Extracting regions, channels, and subsets efficiently
3. Broadcasting -- Writing concise code for element-wise operations across different shapes
4. Reshaping -- Converting between image formats (HWC, CHW, batched) and data layouts
5. Statistics -- Computing histograms, means, distributions, and aggregations
6. Linear Algebra -- Matrix operations for transforms, decompositions, and dimensionality reduction
7. Pandas DataFrames -- Loading, exploring, filtering, and transforming tabular data
8. Data Cleaning -- Handling missing values, outliers, and data quality issues
9. Feature Preparation -- Scaling, encoding, and transforming features for ML algorithms

These skills directly apply to every CV and ML task: loading images, preprocessing data, applying filters, preparing features, and feeding data into models.