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NumPy 1.22.4 Release Notes
NumPy 1.22.4 is a maintenance release that fixes bugs discovered after the 1.22.3 release. In addition, the wheels for this release are built using the recently released Cython 0.29.30, which should f...
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NumPy 1.21.3 Release Notes
NumPy 1.21.3 is a maintenance release that fixes a few bugs discovered after 1.21.2. It also provides 64 bit Python 3.10.0 wheels. Note a few oddities about Python 3.10: There are no 32 bit wheels for...
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NumPy 1.18.2 Release Notes
This small release contains a fix for a performance regression in numpy/random and several bug/maintenance updates. The Python versions supported in this release are 3.5-3.8. Downstream developers sho...
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NumPy 1.17.5 Release Notes
This release contains fixes for bugs reported against NumPy 1.17.4 along with some build improvements. The Python versions supported in this release are 3.5-3.8. Downstream developers should use Cytho...
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NumPy 1.17.1 Release Notes
This release contains a number of fixes for bugs reported against NumPy 1.17.0 along with a few documentation and build improvements. The Python versions supported are 3.5-3.7, note that Python 2.7 ha...
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NumPy 1.16.0 Release Notes
This NumPy release is the last one to support Python 2.7 and will be maintained as a long term release with bug fixes until 2020. Support for Python 3.4 been dropped, the supported Python versions are...
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NumPy 1.14.3 Release Notes
This is a bugfix release for a few bugs reported following the 1.14.2 release: np.lib.recfunctions.fromrecords accepts a list-of-lists, until 1.15 In python2, float types use the new print style when ...
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NumPy 1.13.2 Release Notes
This is a bugfix release for some problems found since 1.13.1. The most important fixes are for CVE-2017-12852 and temporary elision. Users of earlier versions of 1.13 should upgrade. The Python versi...
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NumPy 1.10.0 Release Notes
This release supports Python 2.6 - 2.7 and 3.2 - 3.5. Highlights numpy.distutils now supports parallel compilation via the –parallel/-j argument passed to setup.py build numpy.distutils now supports a...
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NumPy 1.8.1 Release Notes
This is a bugfix only release in the 1.8.x series. Issues fixed gh-4276: Fix mean, var, std methods for object arrays gh-4262: remove insecure mktemp usage gh-2385: absolute(complex(inf)) raises inval...
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Signature file
The syntax specification for signature files (.pyf files) is modeled on the Fortran 90/95 language specification. Almost all Fortran 90/95 standard constructs are understood, both in free and fixed fo...
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Memory Alignment
NumPy alignment goals There are three use-cases related to memory alignment in NumPy (as of 1.14): Creating structured datatypes with fields aligned like in a C-struct. Speeding up copy operations by ...
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Advanced F2PY use cases
Adding user-defined functions to F2PY generated modules User-defined Python C/API functions can be defined inside signature files using usercode and pymethoddef statements (they must be used inside th...
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Glossary
( n ,) A parenthesized number followed by a comma denotes a tuple with one element. The trailing comma distinguishes a one-element tuple from a parenthesized n . -1 In a dimension entry , instructs Nu...
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Array creation
See also Array creation routines Introduction There are 6 general mechanisms for creating arrays: Conversion from other Python structures (i.e. lists and tuples) Intrinsic NumPy array creation functio...
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Indexing on
See also Indexing routines ndarrays can be indexed using the standard Python x[obj] syntax, where x is the array and obj the selection. There are different kinds of indexing available depending on obj...
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I/O with NumPy
Importing data with genfromtxt Defining the input Splitting the lines into columns Skipping lines and choosing columns Choosing the data type Setting the names Tweaking the conversion Shortcut functio...
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Data types
See also Data type objects Array types and conversions between types NumPy supports a much greater variety of numerical types than Python does. This section shows which are available, and how to modif...
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Broadcasting
See also numpy.broadcast The term broadcasting describes how NumPy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is “broadcast” ac...
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Byte-swapping
Introduction to byte ordering and ndarrays The ndarray is an object that provide a python array interface to data in memory. It often happens that the memory that you want to view with an array is not...
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Structured arrays
Introduction Structured arrays are ndarrays whose datatype is a composition of simpler datatypes organized as a sequence of named fields . For example, Here x is a one-dimensional array of length two ...
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Writing custom array containers
Numpy’s dispatch mechanism, introduced in numpy version v1.16 is the recommended approach for writing custom N-dimensional array containers that are compatible with the numpy API and provide custom im...
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Subclassing ndarray
Introduction Subclassing ndarray is relatively simple, but it has some complications compared to other Python objects. On this page we explain the machinery that allows you to subclass ndarray, and th...
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Universal functions (
See also Universal functions (ufunc) A universal function (or ufunc for short) is a function that operates on ndarrays in an element-by-element fashion, supporting array broadcasting , type casting , ...
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