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Master hardware memory, vectorization, ufuncs, and machine learning step by step.
Master Curriculum Roadmap
Filter by tracks or search across array attributes, broadcasting, universal functions, and linear algebra.
01. NumPy Introduction
Beginner • Getting Started
What is NumPy, why is it 50x faster than Python lists, and how does contiguous C-memory work?
02. NumPy Arrays (ndarray)
Beginner • Array Basics
Master 1D vectors, 2D matrices, and 3D tensors built on NumPy's ndarray object.
03. NumPy Array Attributes
Beginner • Array Basics
Inspect ndim, shape, size, dtype, itemsize, nbytes, and strides memory layout.
04. NumPy Data Types (dtype)
Beginner • Array Basics
Explore int8 to int64, float32, float64, boolean, and type casting with astype().
05. Array Creation Functions
Beginner • Array Creation
Generate arrays instantly with zeros(), ones(), full(), eye(), identity(), and diag().
06. Range & Sequence Functions
Beginner • Array Creation
Master np.arange(), np.linspace(), and np.logspace() for sequence generation.
07. NumPy Random Module
Beginner • Random Generation
Generate random integers, uniform floats, standard normal distributions, and lock seeds.
08. NumPy Indexing
Core NumPy • Data Access
Zero-based indexing, negative indexing, and multi-dimensional matrix coordinate selection.
09. NumPy Slicing & Views
Core NumPy • Data Access
Zero-copy memory views with arr[start:stop:step], row slicing, and array reversing.
10. Advanced & Boolean Indexing
Core NumPy • Data Access
Boolean masking, conditional filtering, and index array selection.
11. NumPy Shape & Reshaping
Core NumPy • Array Transformation
Transform dimensions using reshape(), flatten(), ravel(), and transpose (.T).
12. NumPy Array Arithmetic
Core NumPy • Math Operations
Element-wise addition, subtraction, multiplication, division, power, and floor division.
13. NumPy Comparison Operations
Core NumPy • Logic & Comparison
Element-wise comparison operators ==, !=, >, <, >=, <= producing boolean arrays.
14. NumPy Logical Operations
Core NumPy • Logic & Comparison
Combine complex conditions using logical_and(), logical_or(), logical_not(), and & | ~ operators.
15. Universal Functions (ufunc)
Core NumPy • Math Operations
Fast compiled C-level math with np.sqrt, np.abs, np.exp, np.log, np.floor, and np.ceil.
16. Trigonometric Functions
Core NumPy • Math Operations
Calculate sin, cos, tan, inverse trig, and convert between degrees and radians.
17. Aggregate Functions
Core NumPy • Statistics & Reductions
Summarize data with sum(), mean(), min(), max(), std(), var(), and ptp().
18. Cumulative Functions
Core NumPy • Statistics & Reductions
Calculate running totals and products with np.cumsum() and np.cumprod().
19. NumPy Axis Operations
Core NumPy • Statistics & Reductions
Master the Golden Axis Rule: axis=0 collapses rows (↓); axis=1 collapses columns (→).
20. NumPy Broadcasting
Core NumPy • Core Engine
Zero-copy memory expansion rules for operating on arrays of different shapes.
21. NumPy Joining Arrays
Intermediate • Array Manipulation
Combine arrays with concatenate(), vstack(), hstack(), stack(), and column_stack().
22. NumPy Splitting Arrays
Intermediate • Array Manipulation
Divide arrays with split(), array_split(), vsplit(), and hsplit().
23. NumPy Searching
Intermediate • Searching & Sorting
Locate elements and indices using np.where(), np.nonzero(), and np.searchsorted().
24. NumPy Sorting
Intermediate • Searching & Sorting
Sort 1D and 2D arrays with np.sort() and retrieve sorted index order with np.argsort().
25. Min/Max & Index Functions
Intermediate • Statistics & Reductions
Find values and indices of extrema with min(), max(), argmin(), and argmax().
26. NumPy Unique & Set Operations
Intermediate • Set Operations
Deduplicate and analyze sets with unique(), intersect1d(), union1d(), and setdiff1d().
27. NumPy Copy & View
Intermediate • Memory & Performance
Understand memory sharing, arr.base, and when modifying a view alters original data.
28. Missing Values & NaN
Intermediate • Data Cleaning
Detect and handle np.nan using isnan(), nanmean(), nanmax(), and nan_to_num().
29. NumPy Mathematical Functions
Intermediate • Math Operations
Explore add(), subtract(), multiply(), divide(), power(), mod(), and maximum().
30. NumPy Rounding Functions
Intermediate • Math Operations
Master round(), around(), floor(), ceil(), trunc(), and fix() for precision control.
31. NumPy String Operations
Intermediate • Data Cleaning
Vectorized string manipulation with np.char: upper, lower, replace, and strip.
32. NumPy Date & Time
Intermediate • Data Types
Handle timestamps and durations with datetime64 and timedelta64.
33. NumPy Random Distributions
Intermediate • Random Generation
Sample from Uniform, Normal, Binomial, Poisson, and Exponential distributions.
34. NumPy Linear Algebra (np.linalg)
Advanced • Linear Algebra
Dot product, matrix multiplication (@ / matmul), determinant, and matrix inverse.
35. Advanced Linear Algebra
Advanced • Linear Algebra
Eigenvalues, eigenvectors, solving systems of linear equations, and SVD.
36. Polynomial Functions
Advanced • Math Operations
Fit curves, calculate roots, and evaluate polynomials with polyfit() and poly1d().
37. NumPy File Handling
Advanced • I/O & Persistence
Save and load binary .npy/.npz archives and load text CSV datasets with loadtxt().
38. NumPy Memory & Performance
Advanced • Memory & Performance
C-Order vs Fortran-Order, strides, memory flags, and CPU cache pre-fetching.
39. NumPy Vectorization
Advanced • Performance
Eliminate slow Python loops and convert scalar logic into vectorized routines with np.vectorize().
40. Advanced Multi-Index Selection
Advanced • Data Access
Fancy indexing with integer arrays, meshgrid selection, and np.ix_().
41. NumPy Masked Arrays (np.ma)
Advanced • Data Cleaning
Handle invalid and missing data safely with masked arrays in np.ma.
42. Structured Arrays & Record Types
Advanced • Data Types
Define C-style struct records with named fields and heterogeneous dtypes.
43. NumPy Memory Mapping (np.memmap)
Advanced • Memory & Performance
Process 100GB+ datasets directly on disk using virtual memory mapping.
44. Advanced Array Manipulation
Advanced • Array Transformation
Master np.tile(), np.repeat(), np.roll(), np.flip(), np.rot90(), and np.r_ / np.c_.
45. NumPy Statistics & Correlation
Advanced • Statistics & Reductions
Calculate median, percentiles, quantiles, covariance, and correlation matrices (corrcoef).
46. NumPy with Pandas
Practical & Projects • Ecosystem Interop
Convert DataFrames to NumPy with .to_numpy() and apply fast vectorized cleaning.
47. NumPy with Machine Learning
Practical & Projects • Machine Learning
Min-Max scaling, Z-score standardization, One-Hot Encoding, and Euclidean distance.
48. NumPy for Data Analysis
Practical & Projects • Data Analysis
Detect outliers with 3-Sigma rule, filter datasets, and compute group statistics.
49. NumPy for Image Processing
Practical & Projects • Computer Vision
Represent RGB images as 3D (Height, Width, 3) arrays, crop, tint, and grayscale.
50. Practical Real-World Projects
Practical & Projects • Capstone Projects
Build student mark analyzers, sales performance engines, and Monte Carlo simulators.
Drag & Drop Visual NumPy Lab
Chain together array initializers, broadcasting additions, universal functions, and matrix transposes with zero syntax friction. Watch your ndarray transform in real-time!
Why NumPyX?
Traditional tutorials show you syntax and an answer. NumPyX teaches you what happened in hardware memory.
Contiguous C-Memory
Python lists store scattered heap pointers. NumPy arrays are solid blocks in RAM with instant L1/L2 cache pre-fetching.
Zero-Copy Views
Slicing and transposing never duplicate memory. They merely change stride step offsets for instant $O(1)$ operations.
SIMD Vectorization
Eliminate slow Python loops. CPU vector registers execute operations on 8 to 16 numbers simultaneously in a single clock cycle.