Data Analysis

NumPy – core package for scientific computing with Python

NumPy is the fundamental package for scientific computing with Python.

It’s an open source Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.

A wide range of scientific and mathematical Python-based packages use NumPy arrays.

NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

Features include:

  • ndarray object – this encapsulates n-dimensional arrays of homogeneous data types, with many operations being performed in compiled code for performance. It’s a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers.
  • Sophisticated (broadcasting) functions. The term broadcasting describes how NumPy treats arrays with different shapes during arithmetic operations. Broadcasting provides a means of vectorizing array operations so that looping occurs in C instead of Python.
  • Supports a wide range of numerical type.
  • Special matrix type for doing linear algebra, which is a subclass of the array class. Operations on matrix-class arrays are linear algebra operations.
  • Fourier transforms.
  • Random number capabilities:
    • random_sample – uniformly distributed floats over “[0, 1)”.
    • bytes – uniformly distributed random bytes.
    • random_integers – uniformly distributed integers in a given range.
    • permutation – randomly permute a sequence / generate a random sequence.
    • shuffle – randomly permute a sequence in place.
    • seed – seed the random number generator.
    • choice – random sample from 1-D array.
  • Includes f2py – Fortran to Python Interface Generator – offers easy-to-use mechanisms for linking C, C++, and Fortran code directly into Python.

Support: Documentation, Tutorial, Mailing List, GitHub
Developer: NumPy Developers
License: BSD license

NumPy is written in Python. Learn Python with our recommended free books and free tutorials.

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