101 Numpy Exercises for Data Analysis

Abstraction: Graded numpy exercise set for data science practice

Key points:

• 101 exercises across 4 difficulty levels (L1-L4), from basic array creation to advanced stride operations and one-hot encoding
• Core patterns covered: boolean indexing, reshaping, stacking, broadcasting, vectorization with np.vectorize
• Data analysis patterns: normalization, softmax, percentiles, missing-value detection/imputation, Pearson correlation
• Advanced exercises include sliding-window strides (used in CNNs), moving averages, grouped statistics, and rank arrays
• Uses Iris dataset throughout for realistic mixed-type data manipulation examples
• Interactive browser-based execution; no installation required

Connections: Numpy · Array Programming · Data Analysis

Source: https://www.machinelearningplus.com/python/101-numpy-exercises-python/