Lump Sum vs DCA S&P 500: A 3-Month DCA Lost Money More Often Than Investing at Once
Lump sum vs DCA S&P 500 since 1988: lump sum won 65-78% of 453 start months, and DCA barely changed the odds of a losing year. Python script included.
Lump sum vs DCA S&P 500 since 1988: lump sum won 65-78% of 453 start months, and DCA barely changed the odds of a losing year. Python script included.
KDP AI book cover rules and math: disclose AI images, use 1600×2560. 25 designer romance covers cost $20,000; a hybrid $4,000. Python payback calculator.
Multiple pen names on KDP? KDP allows them, but at 1 book a month 3 names need 75 months to reach 25 books each. When splitting pays, with Python math.
How to compare my portfolio to the S&P 500 the right way: a Python shadow benchmark on 5 years of real data turns a fake +1.5-point win into a 3.7-point loss.
A small Python cost model prices a 50,000-word first draft on seven OpenAI and Claude models: from $0.04 to $16.71. About 73% of a naive bill is resending earlier chapters, and prompt caching removes most of it.
Iβm sitting in front of my computer refactoring Python code and have just thought of the following question: Can You Use a Regular Expression with the Python string.startswith() Method? The short answer is no. The string.startswith() method doesn’t allow regular expression inputs. And you don’t need it because regular expressions can already check if a … Read more
While refactoring my Python code, I thought of the following question. Can You Use a Regular Expression with the Python endswith() Method? The simple answer is no because if you can use a regex, you won’t even need endswith()! Instead, use the re.match(regex, string) function from the re module. For example, re.match(“^.*(coffee|cafe)$”, tweet) checks whether … Read more
Are you looking for the best way to become a professional Python developer? After reading this article, you will have a crystal clear plan for learning Python. You donβt need any programming skills for this plan to work. In short, you invest 70% of your learning time in practical projects and 30% in mastering the … Read more
The np.arange() function appears in 21% of the 35 million Github repositories that use the NumPy library! This illustrated tutorial shows you the ins and outs of the NumPy arange function. So let’s get started! What’s the NumPy Arange Function? The np.arange([start,] stop[, step]) function creates a new NumPy array with evenly-spaced integers between start … Read more
A simple and effective way to grow your computer science skills is to master the basics. Knowing the basics sets apart the great coders from the merely intermediate ones. One such basic area in computer science is graph theory, a specific subproblem of which—the pathfinder algorithm—we’ll address in this tutorial. So first things first: What … Read more
Problem Formulation: How to calculate the weighted average of the elements in a NumPy array? Definition weighted average: Each array element has an associated weight. The weighted average is the sum of all array elements, properly weighted, divided by the sum of all weights. Here’s the problem exemplified: Quick solution: Before we discuss the solution … Read more
NumPy is a popular Python library for data science focusing on arrays, vectors, and matrices. This article introduces the np.average() function from the NumPy library. When applied to a 1D array, this function returns the average of the array values. When applied to a 2D array, NumPy simply flattens the array. The result is the … Read more