NumPy (pronounced “numb pie” or sometimes “numb pea”) is an extension to the Python programming language that adds support for large, multi-dimensional arrays, along with an extensive library of high-level mathematical functions to operate on these arrays.
Version | Release Date |
---|---|
1.3.0 | 2009-03-20 |
1.4.0 | 2010-07-21 |
1.5.0 | 2010-11-18 |
1.6.0 | 2011-05-15 |
1.6.1 | 2011-07-24 |
1.6.2 | 2012-05-20 |
1.7.0 | 2013-02-12 |
1.7.1 | 2013-04-07 |
1.7.2 | 2013-12-31 |
1.8.0 | 2013-11-10 |
1.8.1 | 2014-03-26 |
1.8.2 | 2014-08-09 |
1.9.0 | 2014-09-07 |
1.9.1 | 2014-11-02 |
1.9.2 | 2015-03-01 |
1.10.0 | 2015-10-07 |
1.10.1 | 2015-10-12 |
1.10.2 | 2015-12-14 |
1.10.4* | 2016-01-07 |
1.11.0 | 2016-05-29 |
Import the numpy module to use any part of it.
import numpy as np
Most examples will use np
as shorthand for numpy. Assume "np" means "numpy" in code examples.
x = np.array([1,2,3,4])
NumPy is available in the default repositories of most popular Linux distributions and can be installed in the same way that packages in a Linux distribution are usually installed.
Some Linux distributions have different NumPy packages for Python 2.x and Python 3.x. In Ubuntu and Debian, install numpy
at the system level using the APT package manager:
sudo apt-get install python-numpy
sudo apt-get install python3-numpy
For other distributions, use their package managers, like zypper (Suse), yum (Fedora) etc.
numpy
can also be installed with Python's package manager pip
for Python 2 and with pip3
for Python 3:
pip install numpy # install numpy for Python 2
pip3 install numpy # install numpy for Python 3
pip
is available in the default repositories of most popular Linux distributions and can be installed for Python 2 and Python 3 using:
sudo apt-get install python-pip # pip for Python 2
sudo apt-get install python3-pip # pip for Python 3
After installation, use pip
for Python 2 and pip3
for Python 3 to use pip for installing Python packages.
But note that you might need to install many dependencies, which are required to build numpy from source (including development-packages, compilers, fortran etc).
Besides installing numpy
at the system level, it is also common (perhaps even highly recommended) to install numpy
in virtual environments using popular Python packages such as virtualenv
. In Ubuntu, virtualenv
can be installed using:
sudo apt-get install virtualenv
Then, create and activate a virtualenv for either Python 2 or Python 3 and then use pip
to install numpy
:
virtualenv venv # create virtualenv named venv for Python 2
virtualenv venv -p python3 # create virtualenv named venv for Python 3
source venv/bin/activate # activate virtualenv named venv
pip install numpy # use pip for Python 2 and Python 3; do not use pip3 for Python3
The easiest way to set up NumPy on Mac is with pip
pip install numpy
Installation using Conda.
Conda available for Windows, Mac, and Linux
conda install numpy
Numpy installation through pypi (the default package index used by pip) generally fails on Windows computers. The easiest way to install on Windows is by using precompiled binaries.
One source for precompiled wheels of many packages is Christopher Gohkle's site. Choose a version according to your Python version and system. An example for Python 3.5 on a 64 bit system:
numpy-1.11.1+mkl-cp35-cp35m-win_amd64.whl
from herepip install C:\path_to_download\numpy-1.11.1+mkl-cp35-cp35m-win_amd64.whl
If you don't want to mess around with single packages, you can use the Winpython distribution which bundles most packages together and provides a confined environment to work with. Similarly, the Anaconda Python distrubution comes pre-installed with numpy and numerous other common packages.
Another popular source is the conda
package manager, which also supports virtual environments.
conda
.conda install numpy
Jupyter Notebooks are an interactive, browser-based development environment. They were originally developed to run computation python and as such play very well with numpy. To try numpy in a Jupyter notebook without fully installing either on one's local system Rackspace provides free temporary notebooks at tmpnb.org.
Note: that this is not a proprietary service with any sort of upsells. Jupyter is a wholly open-sourced technology developed by UC Berkeley and Cal Poly San Luis Obispo. Rackspace donates this service as part of the development process.
To try numpy
at tmpnb.org:
Welcome to Python.ipynb
or