方法1:优化版(减少存储 & 直接累加) defclassify(number):"""Classify a number as 'perfect', 'abundant', or 'deficient'."""ifnumber <=0:raiseValueError("Classification is only possible for positive integers.") divisor_sum =
defis_perfect_number(num):ifsum_of_factors(num)==num:returnTrueelse:returnFalsedefsum_of_factors(num):sum=0foriinrange(1,num):ifnum%i==0:sum+=ireturnsumfornuminrange(1,1001):ifis_perfect_number(num):print(num) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. ...
This Blog provides a comprehensive guide to creating prime numbers, perfect numbers, and reverse numbers in Python. Learn More about Python Numbers!
编写函数perfect_number: 首先,我们需要定义一个名为perfect_number的函数,它接受一个参数limit,该参数有一个默认值1000。这个函数将用于寻找并返回在1到limit范围内的所有完美数。 python def perfect_number(limit=1000): # 函数体将在下面编写 遍历从1到limit的所有整数: 在函数内部,我们将使用一个for循环...
Python program to print perfect numbers from the given list of integers# Define a function for checking perfect number # and print that number def checkPerfectNum(n): # initialisation i = 2 sum = 1 # iterating till n//2 value while i <= n // 2: # if proper divisor then add it...
Python Code:# Define a function named 'perfect_number' that checks if a number 'n' is a perfect number def perfect_number(n): # Initialize a variable 'sum' to store the sum of factors of 'n' sum = 0 # Iterate through numbers from 1 to 'n-1' using 'x' as the iterator for x...
() else: # Python 3 compatible self.opener = urllib.request.build_opener() self.result = [] self.re_links = re.compile('<a.*?href=.*?<\/a>', re.I) # self.re_element = re.compile('', re.I) # Hardcode at following self.requests = [] for url in urls: if hasattr(urllib...
Python offers three different "copy" options that we will demonstrate using a nested list: importmemory_graphasmgimportcopya=[ [1,2], ['x','y'] ]# a nested list (a list containing lists)# three different ways to make a "copy" of 'a':c1=ac2=copy.copy(a)# equivalent to: a.cop...
while ruby is not the go-to language for data analysis, it can still be used for this purpose. libraries like daru and rubydata provide data manipulation and analysis tools. however, for more extensive statistical computations, languages like python or r might be more suitable. nevertheless, ...
If you're using Python, you might want to check out Gitingest, which is better suited for Python ecosystem and data science workflows: https://github.com/cyclotruc/gitingest 📊 Usage To pack your entire repository: repomix To pack a specific directory: repomix path/to/directory To pack ...