• 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	3-D Visualization	Symbolic Computing	Astronomy Processes	Cognitive Psychology
  QuTiP	Pandas	SciPy	Scikit-image	Mayavi	SymPy	AstroPy	PsychoPy
  PyQuil	statsmodels	PyWavelets	OpenCV	Napari	SunPy
  Qiskit	Seaborn	SpacePy

  Bioinformatics	Bayesian Inference	Mathematical Analysis	Simulation Modeling	Multi-variate Analysis	Geographic Processing	Interactive Computing
  BioPython	PyStan	SciPy	PyDSTool	PyChem	Shapely	Jupyter
  Scikit-Bio	PyMC3	SymPy	GeoPandas	IPython
  PyEnsembl	cvxpy	Folium	Binder
  FEniCS

• 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.

• NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:

  • Extract, Transform, Load: Pandas, Intake, PyJanitor
  • Exploratory analysis: Jupyter, Seaborn, Matplotlib, Altair
  • Model and evaluate: scikit-learn, statsmodels, PyMC3, spaCy
  • Report in a dashboard: Dash, Panel, Voila

  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.

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

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

Trusted Windows (PC) download Python numpy 2.6.212. Virus-free and 100% clean download. Get Python numpy alternative downloads. Download NumPy for Mac - A handy, easy to use and open source toolkit that was developed as a facility for sophisticated scientific computing with Python.

There are different ways to install scikit-learn:

• Install the latest official release. Thisis the best approach for most users. It will provide a stable versionand pre-built packages are available for most platforms.

• Install the version of scikit-learn provided by youroperating system or Python distribution.This is a quick option for those who have operating systems or Pythondistributions that distribute scikit-learn.It might not provide the latest release version.

• Building the package from source. This is best for users who want thelatest-and-greatest features and aren’t afraid of runningbrand-new code. This is also needed for users who wish to contribute to theproject.

1. Install Numpy on Python 2.x version. If you are using Python 2.x, let’s say Python 2.7, then you will have to install the Numpy using the following command. In the terminal, use the pip command to install numpy package. Python -m pip install -U numpy.
2. Available packages. Download location. Official source code (all platforms) and binaries for Windows, Linux and Mac OS X. PyPI page for NumPy. Official source code (all platforms) and binaries for Windows, Linux and Mac OS X. SciPy release page (sources). PyPI page for SciPy (all).

Installing the latest release¶

Then run:

In order to check your installation you can use

Note that in order to avoid potential conflicts with other packages it isstrongly recommended to use a virtual environment, e.g. python3 virtualenv(see python3 virtualenv documentation) or conda environments.

Using an isolated environment makes possible to install a specific version ofscikit-learn and its dependencies independently of any previously installedPython packages.In particular under Linux is it discouraged to install pip packages alongsidethe packages managed by the package manager of the distribution(apt, dnf, pacman…).

Note that you should always remember to activate the environment of your choiceprior to running any Python command whenever you start a new terminal session.

If you have not installed NumPy or SciPy yet, you can also install these usingconda or pip. When using pip, please ensure that binary wheels are used,and NumPy and SciPy are not recompiled from source, which can happen when usingparticular configurations of operating system and hardware (such as Linux ona Raspberry Pi).

If you must install scikit-learn and its dependencies with pip, you can installit as scikit-learn[alldeps].

Scikit-learn plotting capabilities (i.e., functions start with “plot_”and classes end with “Display”) require Matplotlib (>= 2.1.1). For running theexamples Matplotlib >= 2.1.1 is required. A few examples requirescikit-image >= 0.13, a few examples require pandas >= 0.18.0, some examplesrequire seaborn >= 0.9.0.

Warning

Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.Scikit-learn 0.21 supported Python 3.5-3.7.Scikit-learn 0.22 supported Python 3.5-3.8.Scikit-learn now requires Python 3.6 or newer.

Note

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For installing on PyPy, PyPy3-v5.10+, Numpy 1.14.0+, and scipy 1.1.0+are required.

Third party distributions of scikit-learn¶

Some third-party distributions provide versions ofscikit-learn integrated with their package-management systems.

These can make installation and upgrading much easier for users sincethe integration includes the ability to automatically installdependencies (numpy, scipy) that scikit-learn requires.

The following is an incomplete list of OS and python distributionsthat provide their own version of scikit-learn.

Arch Linux¶

Arch Linux’s package is provided through the official repositories aspython-scikit-learn for Python.It can be installed by typing the following command:

Debian/Ubuntu¶

The Debian/Ubuntu package is splitted in three different packages calledpython3-sklearn (python modules), python3-sklearn-lib (low-levelimplementations and bindings), python3-sklearn-doc (documentation).Only the Python 3 version is available in the Debian Buster (the more recentDebian distribution).Packages can be installed using apt-get:

Fedora¶

The Fedora package is called python3-scikit-learn for the python 3 version,the only one available in Fedora30.It can be installed using dnf:

NetBSD¶

scikit-learn is available via pkgsrc-wip:

MacPorts for Mac OSX¶

The MacPorts package is named py<XY>-scikits-learn,where XY denotes the Python version.It can be installed by typing the followingcommand:

Canopy and Anaconda for all supported platforms¶

Canopy and Anaconda both ship a recentversion of scikit-learn, in addition to a large set of scientific pythonlibrary for Windows, Mac OSX and Linux.

Anaconda offers scikit-learn as part of its free distribution.

Intel conda channel¶

Intel maintains a dedicated conda channel that ships scikit-learn:

Autocad 2017 student download mac. This version of scikit-learn comes with alternative solvers for some commonestimators. Those solvers come from the DAAL C++ library and are optimized formulti-core Intel CPUs.

Note that those solvers are not enabled by default, please refer to thedaal4py documentationfor more details.

Compatibility with the standard scikit-learn solvers is checked by running thefull scikit-learn test suite via automated continuous integration as reportedon https://github.com/IntelPython/daal4py.

WinPython for Windows¶

The WinPython project distributesscikit-learn as an additional plugin.

Troubleshooting¶

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Error caused by file path length limit on Windows¶

It can happen that pip fails to install packages when reaching the default pathsize limit of Windows if Python is installed in a nested location such as theAppData folder structure under the user home directory, for instance:

Download Numpy For Python 2.7

In this case it is possible to lift that limit in the Windows registry byusing the regedit tool:

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1. Type “regedit” in the Windows start menu to launch regedit.

2. Go to theComputerHKEY_LOCAL_MACHINESYSTEMCurrentControlSetControlFileSystemkey.

3. Edit the value of the LongPathsEnabled property of that key and setit to 1.

4. Reinstall scikit-learn (ignoring the previous broken installation):