NumPy is an open source numerical computing extension for Python. This tool can be used to store and process large matrices much more efficiently than Python's own nested list structure (which can also be used to represent matrices). NumPy (Numeric Python) provides many advanced numerical programming tools, such as matrix data types, vector processing, and sophisticated arithmetic libraries. Built for rigorous number crunching. It is mostly used by many large financial companies, as well as core scientific computing organizations such as Lawrence Livermore, and NASA uses it to handle some tasks that were originally done using C++, Fortran or Matlab.
The data type in numpy, ndarray type, is different from array.array in the standard library.
Creation of ndarray
>>> import numpy as np >>> a = np.array([2,3,4]) >>> a array([2, 3, 4]) >>> a.dtype dtype('int64') >>> b = np.array([1.2, 3.5, 5.1]) >>> b.dtype dtype('float64')
Two-dimensional array
>>> b = np.array([(1.5,2,3), (4,5,6)]) >>> b array([[ 1.5, 2. , 3. ], [ 4. , 5. , 6. ]])
Specify the type when creating
>>> c = np.array( [ [1,2], [3,4] ], dtype=complex ) >>> c array([[ 1.+0.j, 2.+0.j], [ 3.+0.j, 4.+0.j]])
Create some special matrices
>>> np.zeros( (3,4) ) array([[ 0., 0., 0., 0.], [ 0., 0., 0., 0.], [ 0., 0., 0., 0.]]) >>> np.ones( (2,3,4), dtype=np.int16 ) # dtype can also be specified array([[[ 1, 1, 1, 1], [ 1, 1, 1, 1], [ 1, 1, 1, 1]], [[ 1, 1, 1, 1], [ 1, 1, 1, 1], [ 1, 1, 1, 1]]], dtype=int16) >>> np.empty( (2,3) ) # uninitialized, output may vary array([[ 3.73603959e-262, 6.02658058e-154, 6.55490914e-260], [ 5.30498948e-313, 3.14673309e-307, 1.00000000e+000]])
Create some matrices with specific rules
>>> np.arange( 10, 30, 5 ) array([10, 15, 20, 25]) >>> np.arange( 0, 2, 0.3 ) # it accepts float arguments array([ 0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8]) >>> from numpy import pi >>> np.linspace( 0, 2, 9 ) # 9 numbers from 0 to 2 array([ 0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ]) >>> x = np.linspace( 0, 2*pi, 100 ) # useful to evaluate function at lots of points >>> f = np.sin(x)
Some basic operations
Addition, subtraction, multiplication and division trigonometric functions logical operations
>>> a = np.array( [20,30,40,50] ) >>> b = np.arange( 4 ) >>> b array([0, 1, 2, 3]) >>> c = a-b >>> c array([20, 29, 38, 47]) >>> b**2 array([0, 1, 4, 9]) >>> 10*np.sin(a) array([ 9.12945251, -9.88031624, 7.4511316 , -2.62374854]) >>> a<35 array([ True, True, False, False], dtype=bool)
Matrix operations
There are in matlab. *,./etc.
But in numpy, if you use +,-,×,/, the priority is to perform addition, subtraction, multiplication and division between each point
If two matrices ( Square matrix) can perform operations between elements and perform matrix operations. Operations between elements will be performed first.
>>> import numpy as np >>> A = np.arange(10,20) >>> B = np.arange(20,30) >>> A + B array([30, 32, 34, 36, 38, 40, 42, 44, 46, 48]) >>> A * B array([200, 231, 264, 299, 336, 375, 416, 459, 504, 551]) >>> A / B array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) >>> B / A array([2, 1, 1, 1, 1, 1, 1, 1, 1, 1])
If matrix operations need to be performed, it is usually matrix multiplication
>>> A = np.array([1,1,1,1]) >>> B = np.array([2,2,2,2]) >>> A.reshape(2,2) array([[1, 1], [1, 1]]) >>> B.reshape(2,2) array([[2, 2], [2, 2]]) >>> A * B array([2, 2, 2, 2]) >>> np.dot(A,B) 8 >>> A.dot(B) 8
Some commonly used global functions
>>> B = np.arange(3) >>> B array([0, 1, 2]) >>> np.exp(B) array([ 1. , 2.71828183, 7.3890561 ]) >>> np.sqrt(B) array([ 0. , 1. , 1.41421356]) >>> C = np.array([2., -1., 4.]) >>> np.add(B, C) array([ 2., 0., 6.])
Matrix index slice traversal
>>> a = np.arange(10)**3 >>> a array([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729]) >>> a[2] 8 >>> a[2:5] array([ 8, 27, 64]) >>> a[:6:2] = -1000 # equivalent to a[0:6:2] = -1000; from start to position 6, exclusive, set every 2nd element to -1000 >>> a array([-1000, 1, -1000, 27, -1000, 125, 216, 343, 512, 729]) >>> a[ : :-1] # reversed a array([ 729, 512, 343, 216, 125, -1000, 27, -1000, 1, -1000]) >>> for i in a: ... print(i**(1/3.)) ... nan 1.0 nan 3.0 nan 5.0 6.0 7.0 8.0 9.0
Matrix traversal
>>> import numpy as np >>> b = np.arange(16).reshape(4, 4) >>> for row in b: ... print(row) ... [0 1 2 3] [4 5 6 7] [ 8 9 10 11] [12 13 14 15] >>> for node in b.flat: ... print(node) ... 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Special operations of the matrix
Change the matrix shape- -reshape
>>> a = np.floor(10 * np.random.random((3,4))) >>> a array([[ 6., 5., 1., 5.], [ 5., 5., 8., 9.], [ 5., 5., 9., 7.]]) >>> a.ravel() array([ 6., 5., 1., 5., 5., 5., 8., 9., 5., 5., 9., 7.]) >>> a array([[ 6., 5., 1., 5.], [ 5., 5., 8., 9.], [ 5., 5., 9., 7.]])
The difference between resize and reshape
resize will change the original matrix, reshape will not
>>> a array([[ 6., 5., 1., 5.], [ 5., 5., 8., 9.], [ 5., 5., 9., 7.]]) >>> a.reshape(2,-1) array([[ 6., 5., 1., 5., 5., 5.], [ 8., 9., 5., 5., 9., 7.]]) >>> a array([[ 6., 5., 1., 5.], [ 5., 5., 8., 9.], [ 5., 5., 9., 7.]]) >>> a.resize(2,6) >>> a array([[ 6., 5., 1., 5., 5., 5.], [ 8., 9., 5., 5., 9., 7.]])
merge the matrices
>>> a = np.floor(10*np.random.random((2,2))) >>> a array([[ 8., 8.], [ 0., 0.]]) >>> b = np.floor(10*np.random.random((2,2))) >>> b array([[ 1., 8.], [ 0., 4.]]) >>> np.vstack((a,b)) array([[ 8., 8.], [ 0., 0.], [ 1., 8.], [ 0., 4.]]) >>> np.hstack((a,b)) array([[ 8., 8., 1., 8.], [ 0., 0., 0., 4.]])
The above is the detailed content of Summary of common methods in NumPy. For more information, please follow other related articles on the PHP Chinese website!

Pythonarrayssupportvariousoperations:1)Slicingextractssubsets,2)Appending/Extendingaddselements,3)Insertingplaceselementsatspecificpositions,4)Removingdeleteselements,5)Sorting/Reversingchangesorder,and6)Listcomprehensionscreatenewlistsbasedonexistin

NumPyarraysareessentialforapplicationsrequiringefficientnumericalcomputationsanddatamanipulation.Theyarecrucialindatascience,machinelearning,physics,engineering,andfinanceduetotheirabilitytohandlelarge-scaledataefficiently.Forexample,infinancialanaly

Useanarray.arrayoveralistinPythonwhendealingwithhomogeneousdata,performance-criticalcode,orinterfacingwithCcode.1)HomogeneousData:Arrayssavememorywithtypedelements.2)Performance-CriticalCode:Arraysofferbetterperformancefornumericaloperations.3)Interf

No,notalllistoperationsaresupportedbyarrays,andviceversa.1)Arraysdonotsupportdynamicoperationslikeappendorinsertwithoutresizing,whichimpactsperformance.2)Listsdonotguaranteeconstanttimecomplexityfordirectaccesslikearraysdo.

ToaccesselementsinaPythonlist,useindexing,negativeindexing,slicing,oriteration.1)Indexingstartsat0.2)Negativeindexingaccessesfromtheend.3)Slicingextractsportions.4)Iterationusesforloopsorenumerate.AlwayschecklistlengthtoavoidIndexError.

ArraysinPython,especiallyviaNumPy,arecrucialinscientificcomputingfortheirefficiencyandversatility.1)Theyareusedfornumericaloperations,dataanalysis,andmachinelearning.2)NumPy'simplementationinCensuresfasteroperationsthanPythonlists.3)Arraysenablequick

You can manage different Python versions by using pyenv, venv and Anaconda. 1) Use pyenv to manage multiple Python versions: install pyenv, set global and local versions. 2) Use venv to create a virtual environment to isolate project dependencies. 3) Use Anaconda to manage Python versions in your data science project. 4) Keep the system Python for system-level tasks. Through these tools and strategies, you can effectively manage different versions of Python to ensure the smooth running of the project.

NumPyarrayshaveseveraladvantagesoverstandardPythonarrays:1)TheyaremuchfasterduetoC-basedimplementation,2)Theyaremorememory-efficient,especiallywithlargedatasets,and3)Theyofferoptimized,vectorizedfunctionsformathematicalandstatisticaloperations,making


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

VSCode Windows 64-bit Download
A free and powerful IDE editor launched by Microsoft

MinGW - Minimalist GNU for Windows
This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

EditPlus Chinese cracked version
Small size, syntax highlighting, does not support code prompt function

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.

Dreamweaver Mac version
Visual web development tools
