本文实例讲述了python集合用法。分享给大家供大家参考。具体分析如下:
# sets are unordered collections of unique hashable elements # Python23 tested vegaseat 09mar2005 # Python v2.4 has sets built in import sets print "List the functions within module 'sets':" for funk in dir(sets): print funk # create an empty set set1 = set([]) # now load the set for k in range(10): set1.add(k) print "\nLoaded a set with 0 to 9:" print set1 set1.add(7) print "Tried to add another 7, but it was already there:" print set1 # make a list of fruits as you put them into a basket basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana'] print "\nThe original list of fruits:" print basket # create a set from the list, removes the duplicates fruits = sets.Set(basket) print "\nThe set is unique, but the order has changed:" print fruits # let's get rid of some duplicate words str1 = "Senator Strom Thurmond dressed as as Tarzan" print "\nOriginal string:" print str1 print "A list of the words in the string:" wrdList1 = str1.split() print wrdList1 # now create a set of unique words strSet = sets.Set(wrdList1) print "The set of the words in the string:" print strSet print "Convert set back to string (order has changed!):" print " ".join(strSet) print # comparing two sets, bear with me ... colorSet1 = sets.Set(['red','green','blue','black','orange','white']) colorSet2 = sets.Set(['black','maroon','grey','blue']) print "colorSet1 =", colorSet1 print "colorSet2 =", colorSet2 # same as (colorSet1 - colorSet2) colorSet3 = colorSet1.difference(colorSet2) print "\nThese are the colors in colorSet1 that are not in colorSet2:" print colorSet3 # same as (colorSet1 | colorSet2) colorSet4 = colorSet1.union(colorSet2) print "\nThese are the colors appearing in both sets:" print colorSet4 # same as (colorSet1 ^ colorSet2) colorSet5 = colorSet1.symmetric_difference(colorSet2) print "\nThese are the colors in colorSet1 or in colorSet2, but not both:" print colorSet5 # same as (colorSet1 & colorSet2) colorSet6 = colorSet1.intersection(colorSet2) print "\nThese are the colors common to colorSet1 and colorSet2:" print colorSet6
希望本文所述对大家的Python程序设计有所帮助。

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


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