Backend DevelopmentPython TutorialPython cheat sheet collection, what knowledge points have you mastered?Python cheat sheet collection, what knowledge points have you mastered?
Python is the most popular programming language at the moment. I believe that a large number of novice friends will join the ranks of learning every day. However, no matter how easy a language is to learn, there are still many basic concepts and basic knowledge. For a novice, it is still difficult to master so many in one time. Today’s selection has collected many Python-related knowledge cheat sheets, which can be said to be all-inclusive. In the future, mom will no longer have to worry about everyone not being able to remember any knowledge points!
Python basics
Python basics
This cheat sheet contains all the basic knowledge of Python, from variable data types to list strings, from environment installation to commonly used libraries It can be said to be comprehensive in use.
Beginner's Python
This cheat sheet is also carefully prepared for new scholars and pays more attention to examples. It can be said that there is no problem in mastering the basic usage of Python by following this cheat sheet.
memento Python3
This cheat sheet is a summary of knowledge specifically for Python3, which can be said to be very comprehensive. Contains the use of basic data types, logical and mathematical operations, process control, file IO, etc.
Data Science
Pandas basics
Pandas is the most famous class library in the field of data science, and it has a lot of basic knowledge. This cheat sheet summarizes most of the commonly used Pandas basic knowledge, which is so considerate.
Pandas advance
After learning the basics of Pandas, of course you need to continue to understand the advanced operations of Pandas. This cheat sheet covers a variety of advanced Pandas operations, including pivot tables, grouping, joins, and more.
Numpy
Numpy is another great tool in the field of data science, and it is also the basis of many Python libraries, so mastering this library is also a must. .
Data import
In data science, data is of course the foundation, so importing data is the foundation of the foundation.
Tools
Jupyter
Jupyter is a very famous programming tool in the Python field, with WYSIWYG The powerful effect is the favorite of many Python practitioners.
Okay, today I have shared a lot of Python-related cheat sheets, which are worthy of collection and study by enthusiasts and novices. Let’s learn quickly~
The above is the detailed content of Python cheat sheet collection, what knowledge points have you mastered?. For more information, please follow other related articles on the PHP Chinese website!
Python's Execution Model: Compiled, Interpreted, or Both?May 10, 2025 am 12:04 AMPythonisbothcompiledandinterpreted.WhenyourunaPythonscript,itisfirstcompiledintobytecode,whichisthenexecutedbythePythonVirtualMachine(PVM).Thishybridapproachallowsforplatform-independentcodebutcanbeslowerthannativemachinecodeexecution.
Is Python executed line by line?May 10, 2025 am 12:03 AMPython is not strictly line-by-line execution, but is optimized and conditional execution based on the interpreter mechanism. The interpreter converts the code to bytecode, executed by the PVM, and may precompile constant expressions or optimize loops. Understanding these mechanisms helps optimize code and improve efficiency.
What are the alternatives to concatenate two lists in Python?May 09, 2025 am 12:16 AMThere are many methods to connect two lists in Python: 1. Use operators, which are simple but inefficient in large lists; 2. Use extend method, which is efficient but will modify the original list; 3. Use the = operator, which is both efficient and readable; 4. Use itertools.chain function, which is memory efficient but requires additional import; 5. Use list parsing, which is elegant but may be too complex. The selection method should be based on the code context and requirements.
Python: Efficient Ways to Merge Two ListsMay 09, 2025 am 12:15 AMThere are many ways to merge Python lists: 1. Use operators, which are simple but not memory efficient for large lists; 2. Use extend method, which is efficient but will modify the original list; 3. Use itertools.chain, which is suitable for large data sets; 4. Use * operator, merge small to medium-sized lists in one line of code; 5. Use numpy.concatenate, which is suitable for large data sets and scenarios with high performance requirements; 6. Use append method, which is suitable for small lists but is inefficient. When selecting a method, you need to consider the list size and application scenarios.
Compiled vs Interpreted Languages: pros and consMay 09, 2025 am 12:06 AMCompiledlanguagesofferspeedandsecurity,whileinterpretedlanguagesprovideeaseofuseandportability.1)CompiledlanguageslikeC arefasterandsecurebuthavelongerdevelopmentcyclesandplatformdependency.2)InterpretedlanguageslikePythonareeasiertouseandmoreportab
Python: For and While Loops, the most complete guideMay 09, 2025 am 12:05 AMIn Python, a for loop is used to traverse iterable objects, and a while loop is used to perform operations repeatedly when the condition is satisfied. 1) For loop example: traverse the list and print the elements. 2) While loop example: guess the number game until you guess it right. Mastering cycle principles and optimization techniques can improve code efficiency and reliability.
Python concatenate lists into a stringMay 09, 2025 am 12:02 AMTo concatenate a list into a string, using the join() method in Python is the best choice. 1) Use the join() method to concatenate the list elements into a string, such as ''.join(my_list). 2) For a list containing numbers, convert map(str, numbers) into a string before concatenating. 3) You can use generator expressions for complex formatting, such as ','.join(f'({fruit})'forfruitinfruits). 4) When processing mixed data types, use map(str, mixed_list) to ensure that all elements can be converted into strings. 5) For large lists, use ''.join(large_li
Python's Hybrid Approach: Compilation and Interpretation CombinedMay 08, 2025 am 12:16 AMPythonusesahybridapproach,combiningcompilationtobytecodeandinterpretation.1)Codeiscompiledtoplatform-independentbytecode.2)BytecodeisinterpretedbythePythonVirtualMachine,enhancingefficiencyandportability.
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
Atom editor mac version download
The most popular open source editor
SublimeText3 Linux new version
SublimeText3 Linux latest version
mPDF
mPDF is a PHP library that can generate PDF files from UTF-8 encoded HTML. The original author, Ian Back, wrote mPDF to output PDF files "on the fly" from his website and handle different languages. It is slower than original scripts like HTML2FPDF and produces larger files when using Unicode fonts, but supports CSS styles etc. and has a lot of enhancements. Supports almost all languages, including RTL (Arabic and Hebrew) and CJK (Chinese, Japanese and Korean). Supports nested block-level elements (such as P, DIV),
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.
SublimeText3 English version
Recommended: Win version, supports code prompts!
