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Programming/Development: Python and C++

Submitted by Roy Schestowitz on Wednesday 17th of July 2019 03:53:14 AM Filed under
Development
  • Return the number which is larger than the sum of the remaining numbers in a list with Python

    Given a list of numbers, return a new list consists of the number which is larger than the sum of the remaining numbers in a list or larger than absolute zero. For example, this list [2, 5, 2, 1] will convert to [5, 2, 1] because 5 is greater than 2+1, 2 is greater than 1 and 1 is greater than absolute zero. Another example. [2, 3, -3] will convert to [2, 3] because 2 is greater than 3+(-3) and 3 is greater than -3 but -3 is lesser than 0 which means -3 will not get included into the new list.

  • Jussi Pakkanen: A personal story about 10× development

    During the last few days there has been an ongoing Twitter storm about 10× developers. And like all the ones before it (and all the future ones that will inevitably happen) the debate immediately devolved into name calling and all the other things you'd except from Twitter fights. This blog post is not about that. Instead it is about a personal experience about productivity that I had to experience closer than I would have liked.

    Some years ago I was working for company X on product Y. All in all it was quite a nice experience. We had a small team working on a code base that was pretty good. It had nice tests, not too many bugs, and when issues did arise they were usually easy to fix. Eventually the project was deemed good enough and we were transferred to work on different projects.

    I have no idea what our "industry standard performance multiplier" was when we worked on that project, but for the sake of argument let's call it 1×.

    The project I got transferred to was the thing of nightmares. It was a C++ project and all the bad things that have ever been said about C++ were true about that code base. There was not much code but it was utterly incomprehensible. There were massively deep inheritance hierarchies, , compilation speed was measured in minutes for even the most trivial changes, and so on. It was managed by an architecture astronaut that, as one is wont to do, rewrote existing mature libraries as header only template libraries that were buggy and untested (one could even say untestable).

  • 101 Machine Learning Algorithms for Data Science with Cheat Sheets

    Think of this as the one-stop-shop/dictionary/directory for your machine learning algorithms. The algorithms have been sorted into 9 groups: Anomaly Detection, Association Rule Learning, Classification, Clustering, Dimensional Reduction, Ensemble, Neural Networks, Regression, Regularization. In this post, you'll find 101 machine learning algorithms, including useful infographics to help you know when to use each one (if available).

  • Python for NLP: Developing an Automatic Text Filler using N-Grams

    This is the 15th article in my series of articles on Python for NLP. In my previous article, I explained how to implement TF-IDF approach from scratch in Python. Before that we studied, how to implement bag of words approach from scratch in Python.

    Today, we will study the N-Grams approach and will see how the N-Grams approach can be used to create a simple automatic text filler or suggestion engine. Automatic text filler is a very useful application and is widely used by Google and different smartphones where a user enters some text and the remaining text is automatically populated or suggested by the application.

  • How to Write Pythonic Loops

    One of the easiest ways to spot a developer who has a background in C-style languages and only recently picked up Python is to look at how they loop through a list. In this course, you’ll learn how to take a C-style (Java, PHP, C, C++) loop and turn it into the sort of loop a Python developer would write.

    You can use these techniques to refactor your existing Python for loops and while loops in order to make them easier to read and more maintainable. You’ll learn how to use Python’s range(), xrange(), and enumerate() built-ins to refactor your loops and how to avoid having to keep track of loop indexes manually.

  • Tutorial: Advanced For Loops in Python

    In a previous tutorial, we covered the basics of Python for loops, looking at how to iterate through lists and lists of lists. But there’s a lot more to for loops than looping through lists, and in real-world data science work, you may want to use for loops with other data structures, including numpy arrays and pandas DataFrames.

    This tutorial begins with how to use for loops to iterate through common Python data structures other than lists (like tuples and dictionaries). Then we’ll dig into using for loops in tandem with common Python data science libraries like numpy, pandas, and matplotlib. We’ll also take a closer look at the range() function and how it’s useful when writing for loops.

  • PyCoder’s Weekly: Issue #377 (July 16, 2019)
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