The priority order of Python operators from high to low is as follows: brackets "()", power operation "**", positive and negative signs ", -", multiplication and division "*, /, //, %", addition and subtraction " ", comparison operators ", =, ==, !=", logical not "not", logical AND "and", logical or "or ". In actual use, parentheses can be used to change the precedence of operators.
The operating system for this tutorial: Windows 10 system, Python version 3.11.4, Dell G3 computer.
The order of precedence of Python operators from high to low is as follows:
Brackets ()
Power operation**
Positive and negative signs, -
Multiplication and division*, /, //, %
- # #Addition and subtraction
- Comparison operators, =, ==, !=
- Logical not
- logical and and
- logical or or
print(1 + 2 * 3) # 输出结果为 5 print((1 + 2) * 3) # 输出结果为 92. Exponential operator: **python
print(2 ** 3) # 输出结果为 83. Positive and negative signs: - and (note the positive and negative here Signs are different from addition and subtraction operations because they do not change the priority of addition and subtraction operations)python
print(-2) # 输出结果为 -2 print(+2) # 输出结果为 24. Multiplication, division, modulo: *, /, % 5. Addition and subtraction: , -6. Comparison operators: , >=, !=, ==7. Bitwise operators: & (bitwise AND), | (bitwise OR), ^ (bitwise exclusive OR) 8. Logical operators: not, or, and (note, Python’s Logical operations are from left to right, so the priority of not is higher than and, and the priority of and is higher than or) 9. Identity operator: is, is not10. Member operation Operators: in, not inIt is useful to remember these precedences, especially when you need to combine multiple operators. For example, if you want to take modulo a number and then add 1, you should use parentheses to ensure that the addition is performed before modulo.
The above is the detailed content of How to arrange the priority order of python operators. For more information, please follow other related articles on the PHP Chinese website!

ToappendelementstoaPythonlist,usetheappend()methodforsingleelements,extend()formultipleelements,andinsert()forspecificpositions.1)Useappend()foraddingoneelementattheend.2)Useextend()toaddmultipleelementsefficiently.3)Useinsert()toaddanelementataspeci

TocreateaPythonlist,usesquarebrackets[]andseparateitemswithcommas.1)Listsaredynamicandcanholdmixeddatatypes.2)Useappend(),remove(),andslicingformanipulation.3)Listcomprehensionsareefficientforcreatinglists.4)Becautiouswithlistreferences;usecopy()orsl

In the fields of finance, scientific research, medical care and AI, it is crucial to efficiently store and process numerical data. 1) In finance, using memory mapped files and NumPy libraries can significantly improve data processing speed. 2) In the field of scientific research, HDF5 files are optimized for data storage and retrieval. 3) In medical care, database optimization technologies such as indexing and partitioning improve data query performance. 4) In AI, data sharding and distributed training accelerate model training. System performance and scalability can be significantly improved by choosing the right tools and technologies and weighing trade-offs between storage and processing speeds.

Pythonarraysarecreatedusingthearraymodule,notbuilt-inlikelists.1)Importthearraymodule.2)Specifythetypecode,e.g.,'i'forintegers.3)Initializewithvalues.Arraysofferbettermemoryefficiencyforhomogeneousdatabutlessflexibilitythanlists.

In addition to the shebang line, there are many ways to specify a Python interpreter: 1. Use python commands directly from the command line; 2. Use batch files or shell scripts; 3. Use build tools such as Make or CMake; 4. Use task runners such as Invoke. Each method has its advantages and disadvantages, and it is important to choose the method that suits the needs of the project.

ForhandlinglargedatasetsinPython,useNumPyarraysforbetterperformance.1)NumPyarraysarememory-efficientandfasterfornumericaloperations.2)Avoidunnecessarytypeconversions.3)Leveragevectorizationforreducedtimecomplexity.4)Managememoryusagewithefficientdata

InPython,listsusedynamicmemoryallocationwithover-allocation,whileNumPyarraysallocatefixedmemory.1)Listsallocatemorememorythanneededinitially,resizingwhennecessary.2)NumPyarraysallocateexactmemoryforelements,offeringpredictableusagebutlessflexibility.

InPython, YouCansSpectHedatatYPeyFeLeMeReModelerErnSpAnT.1) UsenPyNeRnRump.1) UsenPyNeRp.DLOATP.PLOATM64, Formor PrecisconTrolatatypes.


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

SublimeText3 Linux new version
SublimeText3 Linux latest version

ZendStudio 13.5.1 Mac
Powerful PHP integrated development environment

DVWA
Damn Vulnerable Web App (DVWA) is a PHP/MySQL web application that is very vulnerable. Its main goals are to be an aid for security professionals to test their skills and tools in a legal environment, to help web developers better understand the process of securing web applications, and to help teachers/students teach/learn in a classroom environment Web application security. The goal of DVWA is to practice some of the most common web vulnerabilities through a simple and straightforward interface, with varying degrees of difficulty. Please note that this software

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.
