NumPy
NumPy is an open-source Python library for numerical computing that provides multidimensional arrays and operations on them.
Fast Python: High performance techniques for large datasets
- How it is used
- Uses multidimensional arrays as the computational basis for image manipulation and faster data analysis with pandas.
- How deep it goes
- Explains views, shape, strides, broadcasting, library configuration and threading; assumes prior NumPy exposure rather than teaching a full introduction.
- Examples
- Uses image-manipulation routines and a benchmarked pandas tip calculation converted to NumPy arrays.
- What you can do after
- Apply it via NumPy and pandas to reduce copying, use whole-array operations and benchmark array-based calculations.
Read how Fast Python: High performance techniques for large datasets explains NumPy →
Statistics Every Programmer Needs
NumPy is a Python library for efficient numerical computation using multidimensional arrays and mathematical operations.
- How it is used
- Supports Python-based numerical computation within quantitative analysis and predictive modeling workflows.
- How deep it goes
- Demonstrates arithmetic, matrix multiplication, and repeated transition calculations, without detailing NumPy internals.
- Examples
- Uses an initial probability vector, a transition matrix, matrix squaring, and US cellular market-share forecasts.
- What you can do after
- Apply it via Python to automate arithmetic and matrix calculations for multi-period state-probability forecasts.
Pandas Workout: 200 exercises to make you a stronger data analyst
NumPy is a Python library providing multidimensional arrays and numerical operations.
- How it is used
- Provides reproducible numerical arrays for constructing and analyzing pandas Series.
- How deep it goes
- Demonstrates seeded integer generation and array shape, without developing broader NumPy array operations.
- Examples
- Uses ten seeded integers, month labels, and a Series average as the concrete exercise.
- What you can do after
- Apply NumPy's random generator to create bounded, reproducible integer arrays.
Read how Pandas Workout: 200 exercises to make you a stronger data analyst explains NumPy →
Geometry for Programmers
NumPy is a Python library for numerical computing with multidimensional arrays and mathematical operations.
- How it is used
- Supports efficient numeric computation, matrix algebra, interpolation workflows, and array-based geometric calculations.
- How deep it goes
- Demonstrates matrix solving, coordinate grids, and vectorization, while stopping short of detailed numerical-method internals.
- Examples
- Uses interpolating data, matrices, linear systems, circle signed distance functions, linspace, meshgrid, and vectorized branching.
- What you can do after
- Apply NumPy to construct arrays, solve linear systems, evaluate functions over grids, and process scalar logic across arrays.
Deep Learning with JAX
A Python library for array-based numerical computing.
- How it is used
- Supports Python numerical computing and practical tensor-processing pipelines such as image loading, filtering, and saving.
- How deep it goes
- Explains the array API and image-array properties through a worked pipeline, while deferring several NumPy–JAX differences.
- Examples
- Uses denoising an image, loading a photo, inspecting shape and rank, and comparing NumPy code with jax.numpy.
- What you can do after
- Apply it via NumPy arrays to represent images, inspect their structure, and build basic numerical-processing pipelines.
Build Your Own Robot: Using Python, CRICKIT, and Raspberry Pi
NumPy is a Python library for numerical computing that provides multidimensional arrays and operations on numerical data.
- How it is used
- Supplies image-array data for OpenCV camera capture and drawing tasks.
- How deep it goes
- Demonstrates ndarray type, shape, dtype, and zero-filled image creation without broader NumPy operations.
- Examples
- Uses a 480×640×3 captured frame and a black array canvas for circles, lines, rectangles, and text.
- What you can do after
- Apply it via OpenCV to create and inspect image arrays for camera and drawing workflows.
Read how Build Your Own Robot: Using Python, CRICKIT, and Raspberry Pi explains NumPy →
Python Concurrency with asyncio
NumPy is a Python library for numerical computing with multidimensional arrays, matrices, and vectorized mathematical operations.
- How it is used
- Uses NumPy for efficient numerical array operations and for selectively accelerating CPU-bound workloads with threads.
- How deep it goes
- Explains arrays, axes, reshaping, performance, GIL release, profiling, and benchmarking, but defers detailed implementation guarantees.
- Examples
- Demonstrates row means over a reshaped four-billion-point array and distributes row calculations through a thread pool.
- What you can do after
- Apply it via NumPy array operations, then evaluate threaded alternatives with profiling and benchmarks.
Related concepts
- Pythonused by · Python is a high-level, general-purpose, dynamically typed programming language known for readable syntax and automatic memory management.
- pandasused by · pandas is an open-source Python library that provides labeled data structures and tools for manipulating and analyzing tabular and other structured data.
- arrayuses · An array is an indexed, ordered collection of elements arranged in one or more dimensions, with elements accessed by position.
- vectorizationuses · Vectorization is the representation of data or computations as vectors or arrays so they can be processed numerically or collectively.
- Cuses · C is a general-purpose, statically typed, compiled procedural programming language providing low-level control over memory and system operations.
- NumPy arrayimplements · A NumPy array, or ndarray, is a multidimensional array object with a defined shape and element data type, designed for efficient numerical computation.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.