Start learning NumPy

Learn NumPy from the first array through broadcasting, linear algebra, real-data operations, and performance. Follow the eleven groups below in order or open a specific lesson.

Introduction to NumPy

• What is NumPy?
• Why use NumPy over Python lists?
• Installing NumPy (pip)
• Importing conventions

NumPy Arrays - Basics

• Creating arrays: array, arange, linspace, zeros, ones, etc.
• Data types in arrays
• Array attributes: shape, size, ndim, itemsize, etc.
• Array Indexing (1D to 3D)
• Array Slicing (1D to 3D)
• Array Element Modifications
• Array Iteration

Array Operations

• Arithmetic operations (element-wise)
• Universal functions (ufuncs)
• Broadcasting
• Comparison Functions
• Aggregation Functions
• Axis-based computations

Linear Algebra with NumPy

• Array Dot Product
• Array Multiplication
• Array Transpose
• Array Inverse
• Array Determinant
• Solving Linear Equations
• Eigenvalues, eigenvectors, diagonalization

Array Reshaping and Manipulation

• Array Reshape
• Array Flatten
• Array Ravel
• Array Concatenation
• Array Stacking
• Splitting Arrays
• Adding/Removing Dimensions

Advanced Indexing and Masking

• Boolean and Fancy Indexing
• Using where, nonzero, argwhere
• Conditional Extraction

Useful NumPy Utilities

• Random module: rand, randn, randint, shuffle
• Sorting Arrays
• Searching Arrays
• Clipping Arrays
• Set Operations and Unique Values

Working with Missing or Invalid Data

• Detecting NaNs
• Detecting Infs
• Replacing/Removing Missing Data
• Masked Arrays

NumPy with Real Data

• Reading/writing data from files (txt, csv)
• Data preprocessing

Performance Optimization

• Vectorization Techniques
• Memory Layout: Views vs Copies
• Timing and Optimizing code

NumPy + SciPy + Pandas

• Overview of SciPy
• Overview of Pandas
• Using NumPy arrays with Pandas
• When to use NumPy vs Pandas