- Numerical computing with NumPy arrays, dtypes, vectorized operations - Data analysis using Pandas DataFrames, grouping, pivoting, and time series - Scientific visualization with Matplotlib plots, layouts, and contour graphics - Real-world data work: files, missing data, binning, and indexing - Applied Python: image processing, probability, and practical projects This book teaches the Python fundamentals required to solve numerical problems in data science and machine learning. The first part focuses on NumPy as the foundation of numerical programming, covering arrays as the core data type, numerical operations, broadcasting, and universal functions, as well as statistics, probability, Boolean masking, and file handling. The second part is devoted to data visualization with Matplotlib, ranging from core concepts to line, bar, histogram, and contour plots. The third part introduces Pandas, including Series and DataFrames, importing and exporting Excel, CSV, and JSON files, handling missing data, and visualization directly within Pandas. The fourth part presents practical applications, including a household budget project, an incomeexpenditure analysis, and an introduction to image processing. The book concludes with a fifth part containing solutions to the numerous exercises that accompany almost every one of the 33 chapters. WHAT‘S INSIDE // Numerical operations on multidimensional arrays/Broadcasting and universal functions (ufuncs)/Discrete &,"e; continuous plots/Bar charts, histograms, and contour plots/Series and DataFrames/Working with Excel, CSV, and JSON files/Handling missing data (NaN)/Data visualization techniques/Image processing funda mentals/Budget tracking and incomeexpenditure analysis