The only prerequisite for installing NumPy is Python itself. If you don’t have Python yet and want the simplest way to get started, we recommend you use the Anaconda Distribution - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science.
NumPy can be installed with conda, with pip, with a package manager on
macOS and Linux, or from source.
For more detailed instructions, consult our Python and NumPy
installation guide below.
CONDA
If you use conda, you can install NumPy from the defaults or conda-forge
channels:
# Best practice, use an environment rather than install in the base env
conda create -n my-env
conda activate my-env
# If you want to install from conda-forge
conda config --env --add channels conda-forge
# The actual install command
conda install numpyPIP
If you use pip, you can install NumPy with:
pip install numpyAlso when using pip, it’s good practice to use a virtual environment - see Reproducible Installs below for why, and this guide for details on using virtual environments.
Installing and managing packages in Python is complicated, there are a number of alternative solutions for most tasks. This guide tries to give the reader a sense of the best (or most popular) solutions, and give clear recommendations. It focuses on users of Python, NumPy, and the PyData (or numerical computing) stack on common operating systems and hardware.
We’ll start with recommendations based on the user’s experience level and operating system of interest. If you’re in between “beginning” and “advanced”, please go with “beginning” if you want to keep things simple, and with “advanced” if you want to work according to best practices that go a longer way in the future.
On all of Windows, macOS, and Linux:
base conda environment minimal, and use one or more
conda environments
to install the package you need for the task or project you’re working on.For users who know, from personal preference or reading about the main differences between conda and pip below, they prefer a pip/PyPI-based solution, we recommend:
Managing packages is a challenging problem, and, as a result, there are lots of tools. For web and general purpose Python development there’s a whole host of tools complementary with pip. For high-performance computing (HPC), Spack is worth considering. For most NumPy users though, conda and pip are the two most popular tools.
The two main tools that install Python packages are pip and conda. Their
functionality partially overlaps (e.g. both can install numpy), however, they
can also work together. We’ll discuss the major differences between pip and
conda here - this is important to understand if you want to manage packages
effectively.
The first difference is that conda is cross-language and it can install Python, while pip is installed for a particular Python on your system and installs other packages to that same Python install only. This also means conda can install non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while pip can’t.
The second difference is that pip installs from the Python Packaging Index (PyPI), while conda installs from its own channels (typically “defaults” or “conda-forge”). PyPI is the largest collection of packages by far, however, all popular packages are available for conda as well.
The third difference is that conda is an integrated solution for managing packages, dependencies and environments, while with pip you may need another tool (there are many!) for dealing with environments or complex dependencies.
As libraries get updated, results from running your code can change, or your code can break completely. It’s important to be able to reconstruct the set of packages and versions you’re using. Best practice is to:
NumPy doesn’t depend on any other Python packages, however, it does depend on an accelerated linear algebra library - typically Intel MKL or OpenBLAS. Users don’t have to worry about installing those (they’re automatically included in all NumPy install methods). Power users may still want to know the details, because the used BLAS can affect performance, behavior and size on disk:
The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. The OpenBLAS libraries are included in the wheel. This makes the wheel larger, and if a user installs (for example) SciPy as well, they will now have two copies of OpenBLAS on disk.
In the conda defaults channel, NumPy is built against Intel MKL. MKL is a separate package that will be installed in the users’ environment when they install NumPy.
In the conda-forge channel, NumPy is built against a dummy “BLAS” package. When a user installs NumPy from conda-forge, that BLAS package then gets installed together with the actual library - this defaults to OpenBLAS, but it can also be MKL (from the defaults channel), or even BLIS or reference BLAS.
The MKL package is a lot larger than OpenBLAS, it’s about 700 MB on disk while OpenBLAS is about 30 MB.
MKL is typically a little faster and more robust than OpenBLAS.
Besides install sizes, performance and robustness, there are two more things to consider:
np.dot, with the number of threads being determined by both a build-time
option and an environment variable. Often all CPU cores will be used. This is
sometimes unexpected for users; NumPy itself doesn’t auto-parallelize any
function calls. It typically yields better performance, but can also be
harmful - for example when using another level of parallelization with Dask,
scikit-learn or multiprocessing.If your installation fails with the message below, see Troubleshooting ImportError.
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
Importing the numpy c-extensions failed. This error can happen for
different reasons, often due to issues with your setup.