"""
To try the examples in the browser:
1. Type code in the input cell and press
Shift + Enter to execute
2. Or copy paste the codeand click on
the "Run" button in the toolbar
"""
# The standard way to import NumPy:
import numpy as np
# Create a 2-D arrayset every second element in
# some rows and find max per row:
x = np.arange(15dtype=np.int64).reshape(35)
x[1:::2] = -99
x
# array([[ 0 1 2 3 4],
# [-99 6-99 8-99],
# [-99 11-99 13-99]])
x.max(axis=1)
# array([ 4 813])
# Generate normally distributed random numbers:
rng = np.random.default_rng()
samples = rng.normal(size=2500)
samplesECOSYSTEM
Nearly every scientist working in Python draws on the power of NumPy.
NumPy brings the computational power of languages like C and Fortran to Pythona language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.
Quantum Computing Statistical Computing Signal Processing Image Processing Graphs and Networks Astronomy Cognitive Psychology Bioinformatics Bayesian Inference Mathematical Analysis Chemistry Geoscience Geographic Processing Architecture & Engineering
NumPy's API is the starting point when libraries are written to exploit innovative hardwarecreate specialized array typesor add capabilities beyond what NumPy provides.
| Array Library | Capabilities & Application areas | |
 | Dask | Distributed arrays and advanced parallelism for analyticsenabling performance at scale. |
 | CuPy | NumPy-compatible array library for GPU-accelerated computing with Python. |
 | JAX | Composable transformations of NumPy programs: differentiatevectorizejust-in-time compilation to GPU/TPU. |
 | Xarray | Labeledindexed multi-dimensional arrays for advanced analytics and visualization. |
 | Sparse | NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. |
 | PyTorch | Deep learning framework that accelerates the path from research prototyping to production deployment. |
 | TensorFlow | An end-to-end platform for machine learning to easily build and deploy ML powered applications. |
 | Arrow | A cross-language development platform for columnar in-memory data and analytics. |
 | xtensor | Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. |
 | Awkward Array | Manipulate JSON-like data with NumPy-like idioms. |
 | uarray | Python backend system that decouples API from implementation; unumpy provides a NumPy API. |
 | tensorly | Tensor learningalgebra and backends to seamlessly use NumPyPyTorchTensorFlow or CuPy. |
 | Blosc2 | Accelerated computation for in-memoryon-diskor remote compressed arrays. |
NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:
- ExtractTransformLoad: PandasIntakePyJanitor
- Exploratory analysis: JupyterSeabornMatplotlibAltair
- Model and evaluate: scikit-learnstatsmodelsPyMCspaCy
- Report in a dashboard: DashPanelVoila
NumPy forms the basis of powerful machine learning libraries like scikit-learn and SciPy. As machine learning growsso does the list of libraries built on NumPy. TensorFlow’s deep learning capabilities have broad applications — among them speech and image recognitiontext-based applicationstime-series analysisand video detection. PyTorchanother deep learning libraryis popular among researchers in computer vision and natural language processing.
Statistical techniques called ensemble methods such as binningbaggingstackingand boosting are among the ML algorithms implemented by tools such as XGBoostLightGBMand CatBoost — one of the fastest inference engines. Yellowbrick and Eli5 offer machine learning visualizations.
NumPy is an essential component in the burgeoning Python visualization landscapewhich includes MatplotlibSeabornPlotlyAltairBokehHolovizVispyNapariand PyVistato name a few.
NumPy’s accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle.
CASE STUDIES
