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NumPy

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</style><table class="infobox vevent"><tbody><tr><th colspan="2" class="infobox-above summary">NumPy</th></tr><tr><td colspan="2" class="infobox-image logo"></td></tr><tr><td colspan="2" class="infobox-image logo"><div class="infobox-caption">Plot of y=sin(x) function, created with NumPy and <a href="Matplotlib" title="Matplotlib">Matplotlib</a> libraries</div></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="Programmer" title="Programmer">Original author(s)</a></th><td class="infobox-data"><a href="Travis_Oliphant" title="Travis Oliphant">Travis Oliphant</a></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="Programmer" title="Programmer">Developer(s)</a></th><td class="infobox-data">Community project</td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;">Initial release</th><td class="infobox-data">As Numeric, 1995 (1995); as NumPy, 2006 (2006)</td></tr><tr style="display: none;"><td colspan="2" class="infobox-full-data"></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="Software_release_life_cycle" title="Software release life cycle">Stable release</a></th><td class="infobox-data"><div style="margin:0px;">2.3.1<a href="#cite_note-wikidata-0427ffff1a2533c64493114ae899e678d4820b54-v20-1">[1]</a>

/ 21 June 2025 (21 June 2025)</div></td></tr><tr style="display:none"><td colspan="2">

</td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="Repository_(version_control)" title="Repository (version control)">Repository</a></th><td class="infobox-data"><style data-mw-deduplicate="TemplateStyles:r1126788409">

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</style><div class="plainlist"><ul><li><a rel="nofollow" class="external text" href="https://github.com/numpy/numpy">github.com/numpy/numpy</a> </li></ul>

</div></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;">Written in</th><td class="infobox-data"><a href="Python_(programming_language)" title="Python (programming language)">Python</a>, <a href="C_(programming_language)" title="C (programming language)">C</a></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="Operating_system" title="Operating system">Operating system</a></th><td class="infobox-data"><a href="Cross-platform" class="mw-redirect" title="Cross-platform">Cross-platform</a></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="Software_categories#Categorization_approaches" title="Software categories">Type</a></th><td class="infobox-data"><a href="Numerical_analysis" title="Numerical analysis">Numerical analysis</a></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="Software_license" title="Software license">License</a></th><td class="infobox-data"><a href="BSD_licenses" title="BSD licenses">BSD</a><a href="#cite_note-numpy_org-2">[2]</a></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;">Website</th><td class="infobox-data"><a rel="nofollow" class="external text" href="https://numpy.org/">numpy.org</a> </td></tr></tbody></table>

NumPy (pronounced /ˈnʌmpaɪ/ NUM-py) is a <a href="Library_(computing)" title="Library (computing)">library</a> for the <a href="Python_(programming_language)" title="Python (programming language)">Python programming language</a>, adding support for large, multi-dimensional <a href="Array_data_structure" class="mw-redirect" title="Array data structure">arrays</a> and <a href="Matrix_(mathematics)" title="Matrix (mathematics)">matrices</a>, along with a large collection of <a href="High-level_programming_language" title="High-level programming language">high-level</a> <a href="Mathematics" title="Mathematics">mathematical</a> <a href="Function_(mathematics)" title="Function (mathematics)">functions</a> to operate on these arrays.<a href="#cite_note-Nature-3">[3]</a> The predecessor of NumPy, Numeric, was originally created by <a href="Jim_Hugunin" title="Jim Hugunin">Jim Hugunin</a> with contributions from several other developers. In 2005, <a href="Travis_Oliphant" title="Travis Oliphant">Travis Oliphant</a> created NumPy by incorporating features of the competing Numarray into Numeric, with extensive modifications. NumPy is <a href="Open-source_software" title="Open-source software">open-source software</a> and has many contributors. NumPy is fiscally sponsored by NumFOCUS.<a href="#cite_note-4">[4]</a>

<div class="mw-heading mw-heading2"><h2 id="History">History</h2></div>

<div class="mw-heading mw-heading3"><h3 id="matrix-sig">matrix-sig</h3></div>

The Python programming language was not originally designed for numerical computing, but attracted the attention of the scientific and engineering community early on. In 1995 the <a href="Special_interest_group" title="Special interest group">special interest group</a> (SIG) matrix-sig was founded with the aim of defining an <a href="Array_data_type" class="mw-redirect" title="Array data type">array</a> computing package; among its members was Python designer and maintainer <a href="Guido_van_Rossum" title="Guido van Rossum">Guido van Rossum</a>, who extended <a href="Python_syntax_and_semantics" title="Python syntax and semantics">Python's syntax</a> (in particular the indexing syntax<a href="#cite_note-5">[5]</a>) to make <a href="Array_programming" title="Array programming">array computing</a> easier.<a href="#cite_note-millman-6">[6]</a>

<div class="mw-heading mw-heading3"><h3 id="Numeric">Numeric</h3></div>

An implementation of a matrix package was completed by Jim Fulton, then generalized by Jim Hugunin and called Numeric<a href="#cite_note-millman-6">[6]</a> (also variously known as the "Numerical Python extensions" or "NumPy"), with influences from the <a href="APL_(programming_language)" title="APL (programming language)">APL</a> family of languages, Basis, <a href="MATLAB" title="MATLAB">MATLAB</a>, <a href="FORTRAN" class="mw-redirect" title="FORTRAN">FORTRAN</a>, <a href="S_(programming_language)" title="S (programming language)">S</a> and <a href="S-PLUS" title="S-PLUS">S+</a>, and others.<a href="#cite_note-cise2-7">[7]</a><a href="#cite_note-numerical-8">[8]</a>

Hugunin, a graduate student at the <a href="Massachusetts_Institute_of_Technology" title="Massachusetts Institute of Technology">Massachusetts Institute of Technology</a> (MIT),<a href="#cite_note-numerical-8">[8]</a>: 10 joined the <a href="Corporation_for_National_Research_Initiatives" title="Corporation for National Research Initiatives">Corporation for National Research Initiatives</a> (CNRI) in 1997 to work on <a href="Jython" title="Jython">JPython</a>,<a href="#cite_note-millman-6">[6]</a> leaving Paul Dubois of <a href="Lawrence_Livermore_National_Laboratory" title="Lawrence Livermore National Laboratory">Lawrence Livermore National Laboratory</a> (LLNL) to take over as maintainer.<a href="#cite_note-numerical-8">[8]</a>: 10 Other early contributors include David Ascher, Konrad Hinsen and <a href="Travis_Oliphant" title="Travis Oliphant">Travis Oliphant</a>.<a href="#cite_note-numerical-8">[8]</a>: 10

<div class="mw-heading mw-heading3"><h3 id="Numarray">Numarray</h3></div>

A new package called Numarray was written as a more flexible replacement for Numeric.<a href="#cite_note-cise-9">[9]</a> Like Numeric, it too is now deprecated.<a href="#cite_note-10">[10]</a><a href="#cite_note-NumPyBook-11">[11]</a> Numarray had faster operations for large arrays, but was slower than Numeric on small ones,<a href="#cite_note-12">[12]</a> so for a time both packages were used in parallel for different use cases. The last version of Numeric (v24.2) was released on 11 November 2005, while the last version of numarray (v1.5.2) was released on 24 August 2006.<a href="#cite_note-13">[13]</a>

There was a desire to get Numeric into the Python standard library, but Guido van Rossum decided that the code was not maintainable in its state then.<a href="#cite_note-14">[14]</a>

<div class="mw-heading mw-heading3"><h3 id="NumPy">NumPy</h3></div>

In early 2005, NumPy developer Travis Oliphant wanted to unify the community around a single array package and ported Numarray's features to Numeric, releasing the result as NumPy 1.0 in 2006.<a href="#cite_note-cise-9">[9]</a> This new project was part of <a href="SciPy" title="SciPy">SciPy</a>. To avoid installing the large SciPy package just to get an array object, this new package was separated and called NumPy. Support for Python 3 was added in 2011 with NumPy version 1.5.0.<a href="#cite_note-15">[15]</a>

In 2011, <a href="PyPy" title="PyPy">PyPy</a> started development on an implementation of the NumPy API for PyPy.<a href="#cite_note-16">[16]</a> As of 2023, it is not yet fully compatible with NumPy.<a href="#cite_note-17">[17]</a>

<div class="mw-heading mw-heading2"><h2 id="Features">Features</h2></div>

NumPy targets the <a href="CPython" title="CPython">CPython</a> <a href="Reference_implementation" title="Reference implementation">reference implementation</a> of Python, which is a non-optimizing <a href="Bytecode" title="Bytecode">bytecode</a> <a href="Interpreter_(computing)" title="Interpreter (computing)">interpreter</a>. <a href="List_of_algorithms#Computational_mathematics" title="List of algorithms">Mathematical algorithms</a> written for this version of Python often run much slower than <a href="Compiler" title="Compiler">compiled</a> equivalents due to the absence of compiler optimization. NumPy addresses the slowness problem partly by providing multidimensional arrays and functions and operators that operate efficiently on arrays; using these requires rewriting some code, mostly <a href="Inner_loop" title="Inner loop">inner loops</a>, using NumPy.

Using NumPy in Python gives functionality comparable to <a href="MATLAB" title="MATLAB">MATLAB</a> since they are both interpreted,<a href="#cite_note-18">[18]</a> and they both allow the user to write fast programs as long as most operations work on <a href="Array" title="Array">arrays</a> or matrices instead of <a href="Scalar_(computing)" class="mw-redirect" title="Scalar (computing)">scalars</a>. In comparison, MATLAB boasts a large number of additional toolboxes, notably <a href="Simulink" title="Simulink">Simulink</a>, whereas NumPy is intrinsically integrated with Python, a more modern and complete <a href="Programming_language" title="Programming language">programming language</a>. Moreover, complementary Python packages are available; SciPy is a library that adds more MATLAB-like functionality and <a href="Matplotlib" title="Matplotlib">Matplotlib</a> is a <a href="Plot_(graphics)" title="Plot (graphics)">plotting</a> package that provides MATLAB-like plotting functionality. Although matlab can perform sparse matrix operations, numpy alone cannot perform such operations and requires the use of the scipy.sparse library. Internally, both MATLAB and NumPy rely on <a href="Basic_Linear_Algebra_Subprograms" title="Basic Linear Algebra Subprograms">BLAS</a> and <a href="LAPACK" title="LAPACK">LAPACK</a> for efficient <a href="Linear_algebra" title="Linear algebra">linear algebra</a> computations.

Python <a href="Language_binding" title="Language binding">bindings</a> of the widely used <a href="Computer_vision" title="Computer vision">computer vision</a> library <a href="OpenCV" title="OpenCV">OpenCV</a> utilize NumPy arrays to store and operate on data.

Since images with multiple channels are simply represented as three-dimensional arrays, indexing, <a href="Array_slicing#1991:_Python" title="Array slicing">slicing</a> or <a href="Mask_(computing)#Image_masks" title="Mask (computing)">masking</a> with other arrays are very efficient ways to access specific pixels of an image.

The NumPy array as universal data structure in OpenCV for images, extracted <a href="Interest_point_detection" class="mw-redirect" title="Interest point detection">feature points</a>, <a href="Kernel_(image_processing)" title="Kernel (image processing)">filter kernels</a> and many more vastly simplifies the programming workflow and <a href="Debugger" title="Debugger">debugging</a>.

Importantly, many NumPy operations release the <a href="Global_interpreter_lock" title="Global interpreter lock">global interpreter lock</a>, which allows for multithreaded processing.<a href="#cite_note-19">[19]</a>

NumPy also provides a C API, which allows Python code to interoperate with external libraries written in low-level languages.<a href="#cite_note-20">[20]</a>

<div class="mw-heading mw-heading3"><h3 id="The_ndarray_data_structure">The ndarray data structure</h3></div>

The core functionality of NumPy is its "ndarray", for n-dimensional array, <a href="Data_structure" title="Data structure">data structure</a>. These arrays are <a href="Stride_of_an_array" title="Stride of an array">strided</a> views on memory.<a href="#cite_note-cise-9">[9]</a> In contrast to Python's built-in list data structure, these arrays are homogeneously typed: all elements of a single array must be of the same type.

Such arrays can also be views into memory buffers allocated by <a href="C_(programming_language)" title="C (programming language)">C</a>/<a href="C%2B%2B" title="C++">C++</a>, <a href="Python_(programming_language)" title="Python (programming language)">Python</a>, and <a href="Fortran" title="Fortran">Fortran</a> extensions to the CPython interpreter without the need to copy data around, giving a degree of compatibility with existing numerical libraries. This functionality is exploited by the SciPy package, which wraps a number of such libraries (notably BLAS and LAPACK). NumPy has built-in support for <a href="Memory-mapped_file" title="Memory-mapped file">memory-mapped</a> ndarrays.<a href="#cite_note-cise-9">[9]</a>

<div class="mw-heading mw-heading3"><h3 id="Limitations">Limitations</h3></div>

Inserting or appending entries to an array is not as trivially possible as it is with Python's lists.

The <code class="mw-highlight mw-highlight-lang-text mw-content-ltr" style="" dir="ltr">np.pad(...)</code> routine to extend arrays actually creates new arrays of the desired shape and padding values, copies the given array into the new one and returns it.

NumPy's <code class="mw-highlight mw-highlight-lang-text mw-content-ltr" style="" dir="ltr">np.concatenate([a1,a2])</code> operation does not actually link the two arrays but returns a new one, filled with the entries from both given arrays in sequence.

Reshaping the dimensionality of an array with <code class="mw-highlight mw-highlight-lang-text mw-content-ltr" style="" dir="ltr">np.reshape(...)</code> is only possible as long as the number of elements in the array does not change.

These circumstances originate from the fact that NumPy's arrays must be views on contiguous <a href="Data_buffer" title="Data buffer">memory buffers</a>.

<a href="Algorithm" title="Algorithm">Algorithms</a> that are not expressible as a vectorized operation will typically run slowly because they must be implemented in "pure Python", while vectorization may increase <a href="Space_complexity" title="Space complexity">memory complexity</a> of some operations from constant to linear, because temporary arrays must be created that are as large as the inputs. Runtime compilation of numerical code has been implemented by several groups to avoid these problems; open source solutions that interoperate with NumPy include numexpr<a href="#cite_note-21">[21]</a> and <a href="Numba" title="Numba">Numba</a>.<a href="#cite_note-22">[22]</a> Cython and Pythran are static-compiling alternatives to these.

Many modern <a href="Big_data" title="Big data">large-scale</a> scientific computing applications have requirements that exceed the capabilities of the NumPy arrays.

For example, NumPy arrays are usually loaded into a computer's <a href="Volatile_memory" title="Volatile memory">memory</a>, which might have insufficient capacity for the analysis of large <a href="Data_set" title="Data set">datasets</a>.

Further, NumPy operations are executed on a single <a href="Central_processing_unit" title="Central processing unit">CPU</a>.

However, many linear algebra operations can be accelerated by executing them on <a href="Computer_cluster" title="Computer cluster">clusters</a> of CPUs or of specialized hardware, such as <a href="Graphics_processing_unit" title="Graphics processing unit">GPUs</a> and <a href="Tensor_Processing_Unit" title="Tensor Processing Unit">TPUs</a>, which many <a href="Deep_learning" title="Deep learning">deep learning</a> applications rely on.

As a result, several alternative array implementations have arisen in the scientific python ecosystem over the recent years, such as <a href="Dask_(software)" title="Dask (software)">Dask</a> for distributed arrays and <a href="TensorFlow" title="TensorFlow">TensorFlow</a> or <a href="Google_JAX" class="mw-redirect" title="Google JAX">JAX</a><a href="#cite_note-23">[23]</a> for computations on GPUs.

Because of its popularity, these often implement a <a href="Subset" title="Subset">subset</a> of NumPy's <a href="API" title="API">API</a> or mimic it, so that users can change their array implementation with minimal changes to their code required.<a href="#cite_note-Nature-3">[3]</a> A library named <a href="CuPy" title="CuPy">CuPy</a>,<a href="#cite_note-24">[24]</a> accelerated by <a href="Nvidia" title="Nvidia">Nvidia</a>'s <a href="CUDA" title="CUDA">CUDA</a> framework, has also shown potential for faster computing, being a '<a href="Drop-in_replacement" title="Drop-in replacement">drop-in replacement</a>' of NumPy.<a href="#cite_note-25">[25]</a>

<div class="mw-heading mw-heading2"><h2 id="Examples">Examples</h2></div>

<div class="mw-highlight mw-highlight-lang-numpy mw-content-ltr" dir="ltr"><pre>import numpy as np

from numpy.random import rand

from numpy.linalg import solve, inv

a = np.array([[1, 2, 3, 4], [3, 4, 6, 7], [5, 9, 0, 5]])

a.transpose()

</pre></div>

<div class="mw-heading mw-heading3"><h3 id="Basic_operations">Basic operations</h3></div>

<div class="mw-highlight mw-highlight-lang-numpy mw-content-ltr" dir="ltr"><pre>>>> a = np.array([1, 2, 3, 6])

>>> b = np.linspace(0, 2, 4) # create an array with four equally spaced points starting with 0 and ending with 2.

>>> c = a - b

>>> c

array([ 1. , 1.33333333, 1.66666667, 4. ])

>>> a**2

array([ 1, 4, 9, 36])

</pre></div>

<div class="mw-heading mw-heading3"><h3 id="Universal_functions">Universal functions</h3></div>

<div class="mw-highlight mw-highlight-lang-numpy mw-content-ltr" dir="ltr"><pre>>>> a = np.linspace(-np.pi, np.pi, 100)

>>> b = np.sin(a)

>>> c = np.cos(a)

>>>

>>> # Functions can take both numbers and arrays as parameters.

>>> np.sin(1)

0.8414709848078965

>>> np.sin(np.array([1, 2, 3]))

array([0.84147098, 0.90929743, 0.14112001])

</pre></div>

<div class="mw-heading mw-heading3"><h3 id="Linear_algebra">Linear algebra</h3></div>

<div class="mw-highlight mw-highlight-lang-numpy mw-content-ltr" dir="ltr"><pre>>>> from numpy.random import rand

>>> from numpy.linalg import solve, inv

>>> a = np.array([[1, 2, 3], [3, 4, 6.7], [5, 9.0, 5]])

>>> a.transpose()

array([[ 1. , 3. , 5. ],

[ 2. , 4. , 9. ],

[ 3. , 6.7, 5. ]])

>>> inv(a)

array([[-2.27683616, 0.96045198, 0.07909605],

[ 1.04519774, -0.56497175, 0.1299435 ],

[ 0.39548023, 0.05649718, -0.11299435]])

>>> b = np.array([3, 2, 1])

>>> solve(a, b) # solve the equation ax = b

array([-4.83050847, 2.13559322, 1.18644068])

>>> c = rand(3, 3) * 20 # create a 3x3 random matrix of values within [0,1] scaled by 20

>>> c

array([[ 3.98732789, 2.47702609, 4.71167924],

[ 9.24410671, 5.5240412 , 10.6468792 ],

[ 10.38136661, 8.44968437, 15.17639591]])

>>> np.dot(a, c) # matrix multiplication

array([[ 53.61964114, 38.8741616 , 71.53462537],

[ 118.4935668 , 86.14012835, 158.40440712],

[ 155.04043289, 104.3499231 , 195.26228855]])

>>> a @ c # Starting with Python 3.5 and NumPy 1.10

</pre></div>

<div class="mw-heading mw-heading3"><h3 id="Multidimensional_arrays">Multidimensional arrays</h3></div>

<div class="mw-highlight mw-highlight-lang-numpy mw-content-ltr" dir="ltr"><pre>>>> M = np.zeros(shape=(2, 3, 5, 7, 11))

>>> T = np.transpose(M, (4, 2, 1, 3, 0))

>>> T.shape

(11, 5, 3, 7, 2)

</pre></div>

<div class="mw-heading mw-heading3"><h3 id="Incorporation_with_OpenCV">Incorporation with OpenCV</h3></div>

<div class="mw-highlight mw-highlight-lang-numpy mw-content-ltr" dir="ltr"><pre>>>> import numpy as np

>>> import cv2

>>> r = np.reshape(np.arange(256*256)%256,(256,256)) # 256x256 pixel array with a horizontal gradient from 0 to 255 for the red color channel

>>> g = np.zeros_like(r) # array of same size and type as r but filled with 0s for the green color channel

>>> b = r.T # transposed r will give a vertical gradient for the blue color channel

>>> cv2.imwrite("gradients.png", np.dstack([b,g,r])) # OpenCV images are interpreted as BGR, the depth-stacked array will be written to an 8bit RGB PNG-file called "gradients.png"

True

</pre></div>

<div class="mw-heading mw-heading3"><h3 id="Nearest-neighbor_search">Nearest-neighbor search</h3></div>

Functional Python and vectorized NumPy version.

<div class="mw-highlight mw-highlight-lang-numpy mw-content-ltr" dir="ltr"><pre>>>> # # # Functional Python # # #

>>> points = [[9,2,8],[4,7,2],[3,4,4],[5,6,9],[5,0,7],[8,2,7],[0,3,2],[7,3,0],[6,1,1],[2,9,6]]

>>> qPoint = [4,5,3]

>>> edistance = lambda a,b: sum((a1-b1)**2 for a1,b1 in zip(a,b))**0.5 # Lambda function for calculating the Euclidean distance of two vectors

>>> nearest = min((edistance(i,qpoint),i) for i in points)[1]# Compute all Euclidean distances at once and return the nearest point

>>> print("Nearest point to q: ",nearest)

Nearest point to q: [3, 4, 4]

>>> # # # Equivalent NumPy vectorization # # #

>>> import numpy as np

>>> points = np.array([[9,2,8],[4,7,2],[3,4,4],[5,6,9],[5,0,7],[8,2,7],[0,3,2],[7,3,0],[6,1,1],[2,9,6]])

>>> qPoint = np.array([4,5,3])

>>> minIdx = np.argmin(np.linalg.norm(points-qPoint, axis=1)) # compute all euclidean distances at once and return the index of the smallest one

>>> print(f"Nearest point to q: {points[minIdx]}")

Nearest point to q: [3 4 4]

</pre></div>

Quickly wrap native code for faster scripts.<a href="#cite_note-26">[26]</a><a href="#cite_note-27">[27]</a><a href="#cite_note-28">[28]</a>

<div class="mw-highlight mw-highlight-lang-fortran mw-content-ltr" dir="ltr"><pre>! Python Fortran native code call example

! f2py -c -m foo *.f90

! Compile Fortran into python named module using intent statements

! Fortran subroutines only not functions--easier than JNI with C wrapper

! requires gfortran and make

subroutine ftest(a, b, n, c, d)

implicit none

integer, intent(in) :: a, b, n

integer, intent(out) :: c, d

integer :: i

c = 0

do i = 1, n

c = a + b + c

end do

d = (c * n) * (-1)

end subroutine ftest

</pre></div>

<div class="mw-highlight mw-highlight-lang-pycon mw-content-ltr" dir="ltr"><pre>>>> import numpy as np

>>> import foo

>>> a = foo.ftest(1, 2, 3) # or c,d = instead of a.c and a.d

>>> print(a)

(9,-27)

>>> help("foo.ftest") # foo.ftest.__doc__

</pre></div>

<ul><li><a href="Array_programming" title="Array programming">Array programming</a></li>

<li><a href="List_of_numerical-analysis_software" title="List of numerical-analysis software">List of numerical-analysis software</a></li>

<li><a href="Theano_(software)" title="Theano (software)">Theano (software)</a></li>

<li><a href="Matplotlib" title="Matplotlib">Matplotlib</a></li>

<li><a href="Fortran" title="Fortran">Fortran</a></li>

<li><a href="Row-_and_column-major_order" title="Row- and column-major order">Row- and column-major order</a></li>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

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</ol></div>

<div class="mw-heading mw-heading2"><h2 id="Further_reading">Further reading</h2></div>

<ul><li><cite id="CITEREFMcKinney2022" class="citation book cs1">McKinney, Wes (2022). <a rel="nofollow" class="external text" href="https://wesmckinney.com/book/">Python for Data Analysis</a> (3rd ed.). O'Reilly. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-1098104030</bdi>.</cite></li>

<li><cite id="CITEREFBressert2012" class="citation book cs1">Bressert, Eli (2012). Scipy and Numpy: An Overview for Developers. O'Reilly. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-1-4493-0546-8</bdi>.</cite></li>

<li><cite id="CITEREFVanderPlas2016" class="citation book cs1">VanderPlas, Jake (2016). "Introduction to NumPy". Python Data Science Handbook: Essential Tools for Working with Data. O'Reilly. pp. 33–96. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-1-4919-1205-8</bdi>.</cite></li></ul>

<div class="mw-heading mw-heading2"><h2 id="External_links">External links</h2></div>

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<ul><li><a rel="nofollow" class="external text" href="https://numpy.org/">Official website</a> </li>

<li><a rel="nofollow" class="external text" href="https://numpy.org/numpy-tutorials/">NumPy tutorials</a></li>

<li><a rel="nofollow" class="external text" href="https://scipy.github.io/old-wiki/pages/History_of_SciPy">History of NumPy</a></li></ul>

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</style><div id="Scientific_software_in_Python63" style="font-size:114%;margin:0 4em">Scientific software in <a href="Python_(programming_language)" title="Python (programming language)">Python</a></div></th></tr><tr><td colspan="2" class="navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul>

<li><a href="SciPy" title="SciPy">SciPy</a></li>

<li><a href="Matplotlib" title="Matplotlib">matplotlib</a></li>

<li><a href="Pandas_(software)" title="Pandas (software)">pandas</a></li>

<li><a href="Scikit-learn" title="Scikit-learn">scikit-learn</a></li>

<li><a href="Scikit-image" title="Scikit-image">scikit-image</a></li>

<li><a href="MayaVi" title="MayaVi">MayaVi</a></li>

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