NumPy Tutorial

Master NumPy from scratch with our comprehensive, interactive curriculum — 50 lessons from Beginner to Advanced.

Learn NumPy from scratch — ndarrays, indexing, broadcasting, ufuncs, linear algebra and vectorized performance — free interactive lessons.

Part of the free Numpy course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

👋 New to NumPy? Some Python is all you need.

Start with Lesson 1 and build real numerical-computing skills step by step — at your own pace.

Lessons in this course

• Introduction to NumPy — What NumPy is, why it's fast, and how to install and import it
• The ndarray: NumPy's Core Object — Understand the n-dimensional array that powers all of NumPy
• Creating Arrays (array, zeros, arange, linspace) — Build arrays from lists and with built-in constructors
• Array Data Types (dtypes) — Control memory and precision with int32, float64, bool and more
• Array Attributes: shape, ndim, size — Inspect the structure of any array with its key attributes
• Indexing and Slicing Arrays — Access and modify elements, rows, columns, and sub-grids
• Boolean & Fancy Indexing — Select elements with masks and integer index arrays
• Reshaping and Flattening — Change array shape with reshape, ravel, flatten, and transpose
• Element-wise Operations & Universal Functions — Apply fast vectorized math with ufuncs like add, sqrt, and exp
• Broadcasting Explained — Operate on arrays of different shapes without writing loops
• Aggregations: sum, mean, axis — Summarize data along rows and columns with the axis argument
• Stacking & Splitting Arrays — Combine and divide arrays with stack, concatenate, and split
• Copies vs Views — Know when slicing shares memory and when it copies data
• Random Number Generation — Generate reproducible random data with the modern Generator API
• Linear Algebra with numpy.linalg — Solve systems, find determinants, inverses, and eigenvalues
• Matrix Operations & Dot Products — Multiply matrices with dot and the @ operator
• Conditions: where, select, clip — Build new arrays from conditional logic without loops
• Sorting, Searching & Counting — Order data and find values with sort, argsort, and searchsorted
• Structured & Record Arrays — Store tabular data with named, typed fields in one array
• File I/O: save, load, savetxt — Persist arrays to .npy, .npz, and text files
• Performance & Vectorization vs Loops — Replace slow Python loops with fast vectorized NumPy code
• Splitting Arrays: split, hsplit & vsplit — Divide one array into many with split, hsplit, vsplit, and array_split
• The axis Parameter Explained — Finally understand axis=0 vs axis=1 and keepdims on multi-dimensional arrays
• Fancy Indexing with Integer Arrays — Select and reorder elements with arrays of integer indices
• Handling NaN & Infinity — Detect and survive NaN and inf with isnan, isfinite, and nan-aware reductions
• Unique Values & Counting — Find and tally distinct values with unique, bincount, and count_nonzero
• Set Operations on Arrays — Compare arrays with intersect1d, union1d, setdiff1d, and isin
• Rounding & Clipping — Round, floor, ceil, and constrain values into a range with clip
• Cumulative & Difference Functions — Build running totals and deltas with cumsum, cumprod, and diff
• Checkpoint: Array Operations — Combine axis reductions, fancy indexing, NaN handling and set ops — then a quiz
• meshgrid & Coordinate Grids — Build coordinate grids to evaluate functions over a 2D plane
• Repeating Arrays: tile & repeat — Duplicate data with tile and repeat — and know the difference
• Adding Axes: newaxis & expand_dims — Reshape for broadcasting by inserting and squeezing size-1 dimensions
• Transposing & Swapping Axes — Reorder dimensions with .T, transpose, swapaxes, and moveaxis
• Dates & Times with datetime64 — Do date arithmetic with datetime64 and timedelta64 and their units
• Polynomials: polyfit & poly1d — Fit and evaluate polynomials with polyfit, poly1d, polyval, and roots
• Vectorizing Python Functions — Apply Python functions over arrays with vectorize and apply_along_axis
• Einstein Summation: einsum — Express dot products, matmuls, transposes, and traces with one notation
• Checkpoint: Numerical Computing — Combine grids, polynomial fits and einsum in a build — then a quiz
• Masked Arrays (np.ma) — Flag and skip invalid entries with masked arrays and masked reductions
• Fourier Transforms (np.fft) — Analyse frequency content of signals with the FFT and rfft
• Interpolation (np.interp) — Estimate values between known data points with linear interpolation
• Histograms & Binning — Bin and count data with histogram, digitize, and bincount
• Memory Layout & Strides — C vs Fortran order, strides, and why contiguity affects speed
• ufunc Methods (reduce, accumulate, outer) — Unlock reduce, accumulate, outer, and at on any universal function
• Statistics (percentile, median, std) — Summaries, percentiles, correlation and covariance along an axis
• Linear Algebra: solve, svd, eig — Solve systems and decompose matrices with numpy.linalg
• NaN-Aware Functions (nanmean, nansum) — Compute statistics that ignore NaN instead of poisoning the result
• Checkpoint: Scientific Computing — Combine histograms, robust stats and linear algebra in a build — then a quiz
• Capstone: Image as an Array — Treat an image as a 3D array and manipulate its pixels

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