NumPy
The fundamental package for scientific computing with Python
D&I Grant from CZI
Including NumPy, SciPy, Matplotlib and Pandas

Powerful N-dimensional arrays
Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.

Numerical computing tools
NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.

Interoperable
NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.

Performant
The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.

Easy to use
NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.

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Open source
Distributed under a liberal BSD license, NumPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.

Try NumPy
Enable the interactive shell

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Betuy Fahrrad Lenkertasche Wasserdicht, 3.5L Fahrradtasche Handy
Betuy Fahrrad Lenkertasche Wasserdicht, 3.5L Fahrradtasche Handy

  • 1. Große Kapazität amp; Wasserdicht. 3.5L große Kapazität Radfahren vorne Lenkertasche, aus wasserdichtem reißfest gestrickt Polyester-Material, solide und langlebig. Die Beutelgröße beträgt 24,5×15×17,5cm.
  • 2. Touch Screen amp; Internal Aluminium Foil Design. Mit transparenten PVC-empfindliche Touchscreen, können Sie Ihr Telefon jedes Mal. Die Aluminiumfolie hat die Funktion der warmen und kühlen Konservierung.
  • 3. Safe Reflective Strip. Für Ihre Sicherheit bei Nacht ist die Vorderseite der Tasche mit reflektierenden Streifen ausgestattet, die selbst bei jedem Wetter gut sichtbar und reflektierend sind.
  • 4. Netztasche und schnelle Installation. Die Netztasche kann einige kleine Werkzeuge. Mit 3 Magic Sticks Design kann die Fahrrad Lenkertasche problemlos ohne Werkzeug am Fahrrad installiert werden.
  • 5. Breit Anwendung. Ideal für die meisten Arten von Fahrrädern, wie schnelles Fahren, Rennrad, Faltrad und Mountainbike. Diese Tasche ist ein perfektes Geschenk für Radfahrer.
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Produktbeschreibungen

Spezifikationen:

Artikel Größe: 24,5 * 15 * 17,5 cm
Material: PVC + Polyester + Aluminiumfolie
Kapazität: 3.5L
Farbe schwarz
Geeignet für: Mountainbike, Faltrad, Rennrad, etc
Artikel Gewicht: 300g

Eigenschaften:

- Big Capacity: Diese Fahrradtasche mit 3.5L Stauraum bietet Platz für viele Kleinigkeiten wie Schlüssel, Geldbörse, Stift, kleine Reparaturwerkzeuge usw.
- Wasserdicht: Hergestellt aus wasserdichtem Polyestermaterial, hilft es, Ihr Handy auch bei regnerischen Tagen fern von Wasser zu halten.
- Empfindlichkeit: Es ist bequemer, das Telefon während des Radfahrens zu bedienen.
- Warm- und Kühlkonservierung - Aluminiumfolie mit innenliegender Aluminiumfolie hat die Funktion der Warm- und Kühlkonservierung.
- Safe Reflektierender Streifen: Es gibt reflektierende Streifen auf der Vorderseite der Tasche, Sie können nachts sicherer fahren.

Paketliste:

1 * Fahrrad Lenkertasche

  • Nearly every scientist working in Python draws on the power of NumPy.

    NumPy brings the computational power of languages like C and Fortran to Python, a 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 Processes Cognitive Psychology
    QuTiP Pandas SciPy Scikit-image NetworkX AstroPy PsychoPy
    PyQuil statsmodels PyWavelets OpenCV graph-tool SunPy
    Qiskit Xarray python-control Mahotas igraph SpacePy
    Seaborn PyGSP
    Bioinformatics Bayesian Inference Mathematical Analysis Chemistry Geoscience Geographic Processing Architecture & Engineering
    BioPython PyStan SciPy Cantera Pangeo Shapely COMPAS
    Scikit-Bio PyMC3 SymPy MDAnalysis Simpeg GeoPandas City Energy Analyst
    PyEnsembl ArviZ cvxpy RDKit ObsPy Folium Sverchok
    ETE emcee FEniCS Fatiando a Terra
  • NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides.

    Array Library Capabilities & Application areas
    Dask Distributed arrays and advanced parallelism for analytics, enabling performance at scale.
    CuPy NumPy-compatible array library for GPU-accelerated computing with Python.
    JAX Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU.
    Xarray Labeled, indexed 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.
    MXNet Deep learning framework suited for flexible research prototyping and production.
    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.
    XND Develop libraries for array computing, recreating NumPy's foundational concepts.
    uarray Python backend system that decouples API from implementation; unumpy provides a NumPy API.
    tensorly Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy.
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    NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:

    For high data volumes, Dask and Ray are designed to scale. Stable deployments rely on data versioning (DVC), experiment tracking (MLFlow), and workflow automation (Airflow and Prefect).

  • NumPy forms the basis of powerful machine learning libraries like scikit-learn and SciPy. As machine learning grows, so does the list of libraries built on NumPy. TensorFlow’s deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. PyTorch, another deep learning library, is popular among researchers in computer vision and natural language processing. MXNet is another AI package, providing blueprints and templates for deep learning.

    Statistical techniques called ensemble methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as XGBoost, LightGBM, and CatBoost — one of the fastest inference engines. Yellowbrick and Eli5 offer machine learning visualizations.

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    NumPy is an essential component in the burgeoning Python visualization landscape, which includes Matplotlib, Seaborn, Plotly, Altair, Bokeh, Holoviz, Vispy, Napari, and PyVista, to name a few.

    NumPy’s accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle.

CASE STUDIES