How to install numpy array in python?
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Before you can import numpy, you first need to install it. There are two ways to install numpy:
The simplest way to install numpy is to use the pip package manager to download the binary version from the Python Package Index (PyPI.org) and install it on your system using the following command: pip install numpy Afterward, you can check if Numpy is properly installed by starting Python and running the following lines of codes. import numpy as np np.__version__ If everything is properly installed, you should see an output similar to this: '1.15.1' If your company requires that all packages be built from source, you’ll need to manually compile the Numpy library, which means you’ll need to install the following on your local system:
Compiling from source can get quite complex, what with environment setup, scripts and patches, not to mention resolving any dependency conflicts or errors that may occur. Instead, consider using the ActivateState Platform to automatically build and package it for you. Get a version of Python that’s pre-compiled for Data ScienceWhile the open source distribution of Python may be satisfactory for an individual, it doesn’t always meet the support, security, or platform requirements of large organizations. This is why organizations choose ActivePython for their data science, big data processing and statistical analysis needs. Pre-bundled with the most important packages Data Scientists need, ActivePython is pre-compiled so you and your team don’t have to waste time configuring the open source distribution. You can focus on what’s important–spending more time building algorithms and predictive models against your big data sources, and less time on system configuration. Some Popular Python Packages for Data Science/Big Data/Machine Learning You Get Pre-compiled – with ActivePython
With deep roots in open source, and as a founding member of the Python Foundation, ActiveState actively contributes to the Python community. We offer the convenience, security and support that your enterprise needs while being compatible with the open source distribution of Python. Download ActivePython Community Edition to get started or contact us to learn more about using ActivePython in your organization. You can also start by trying our mini ML runtime for Linux or Windows that includes most of the popular packages for Machine Learning and Data Science, pre-compiled and ready to for use in projects ranging from recommendation engines to dashboards. 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 If you use
PIP If you use Also 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. Python and NumPy installation guideInstalling 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. RecommendationsWe’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. Beginning usersOn all of Windows, macOS, and Linux:
Advanced usersConda
Alternative if you prefer pip/PyPIFor 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:
Python package managementManaging 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. Pip & condaThe two
main tools that install Python packages are 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. Reproducible installsAs 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 packages & accelerated linear algebra librariesNumPy 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:
Besides install sizes, performance and robustness, there are two more things to consider:
TroubleshootingIf your installation fails with the message below, see Troubleshooting ImportError.
How do I install NumPy in Python?PYTHON 2.7. Press command (⌘) + Space Bar to open Spotlight search. Type in Terminal and press enter.. In the terminal, use the pip command to install numpy package.. Once the package is installed successfully, type python to get into python prompt. Notice the python version is displayed too.. Where is NumPy installed in Python?Go to Python -> site-packages folder. There you should be able to find numpy and the numpy distribution info folder. If any of the above is true then you installed numpy successfully.
Is NumPy automatically installed with Python?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.
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