Language Selection

English French German Italian Portuguese Spanish

Programming Leftovers

Filed under
Development

More in Tux Machines

Python Programming

  • Dynamically Regrouping QuerySets In Templates - Building SaaS #73

    In this episode, we worked on a new view to display course resources. While building out the template, I used some template tags to dynamically regroup a queryset into a more useful data format for rendering. I started a new view before the stream to display content, but I had not filled it in before the stream started. We added new data to the context, and did some adjustments to the URL based on the required inputs for the view. Once I had the data, I quickly iterated in the template to see the parts that I included. I needed to display the course resources in a different way from how the queryset provided them so I used the built-in regroup template tag to organize the data differently. regroup saved me from doing a bunch of manipulation in the view code.

  • PyCharm: Webinar: “virtualenv – a deep dive” with Bernat Gabor

    virtualenv is a tool that builds virtual environments for Python. It was first created in September 2007 and just went through a rewrite from scratch. Did you ever want to know what parts virtual environments can be broken down into? Or how they work? And how does virtualenv differ from the Python builtin venv? This is the webinar you want.

  • Python 3.8.6

    This is the sixth maintenance release of Python 3.8 The Python 3.8 series is the newest major release of the Python programming language, and it contains many new features and optimizations.

  • Python 3.8.6 is now available

    Python 3.8.6 is the sixth maintenance release of Python 3.8.

  • Facial Detection in Python with OpenCV

    Facial detection is a powerful and common use-case of Machine Learning. It can be used to automatize manual tasks such as school attendance and law enforcement. In the other hand, it can be used for biometric authorization. In this article, we'll perform facial detection in Python, using OpenCV. [...] With OpenCV installed, we can import it as cv2 in our code. To read an image in, we will use the imread() function, along with the path to the image we want to process. The imread() function simply loads the image from the specified file in an ndarray. If the image could not be read, for example in case of a missing file or an unsupported format, the function will return None.

  • Sending Emails With CSV Attachment Using Python

    In this tutorial, we will learn how to send emails with CSV attachments using Python. Pre-Requirements I am assuming you already have an SMTP server setup if not you can use the Gmail SMTP or Maligun or anything similar to that.

  • Sending Email With Zip Files Using Python

    In this tutorial, we will learn how to send emails with zip files using Python’s built-in modules. Pre-Requirements I am assuming that you already have an SMTP (Simple Mail Transfer Protocol ) server setup if not you can use Gmail SMTP or something like mailgun. A simple google search will land you on multiple ways to get free SMTP servers.

  • Is Python better than R for data science?

    If data science is going to remain a main-stream in the next 5 years, it needs to add value not only in terms of proof of concept (as it is doing now) but also in terms of production (as it is failing in over 70% of cases, as Gartner recently surveyed). While R is an absolute winner in terms of classical pattern recognition libraries and statistical methods, python has a better ability to write production-ready codes. Above point raises another important point, that is best practices of software engineering (e.g., uml architecture designs, unit testing, coding review, scrum) are going to be absolute requirements in near future for data scientists, in addition to the expected knowledge in machine learning and statistics. The reason is that proper software, production ready, codes require proper architecture design, with proper reviews and testing.

WhatIP – Graphical Tool to Tell Public / Local IP Address, Ports, Local Devices

Want to check your IP address, listening ports, or local network devices but hate Linux commands? Well, What IP is a simple graphical tool to do the job. What IP is a free open-source tool written in Python 3 with GTK+ 3 framework. Read more

20+ Distraction-free Text Editors for Linux, Windows, macOS and The Cloud

While writing, it's essential to have a distraction-free environment. That will help the writer formulate his ideas into words. Most of the text processor software and document editor programs are full of tools, customization options which make them distracting the writer, and they already take large portion of the screen. Distraction-free editors are required by writers, screenwriters, novelists, researchers and journalists. Distraction-free modes have several criteria that starts from minimal user-interface, full-screen mode, few tools in the user-interface and focus mode. Read more

Leftovers: Canonical on Banks, Raspberry Pi and Curl

  • A ‘Connected’ Bank – The power of data and analytics

    The next 10 years will redefine banking. What will differentiate top banks from their competitors? Data and derived insights. Banks across the globe have been immersed in their digital agenda and with customers adopting digital banking channels aggressively, banks are collecting massive volumes of data on how customers are interacting at various touch points. Apart from the health of balance sheets, what will differentiate top banks from the competition is how effectively these data assets will be used to make banking simpler and improve their products and services. The challenge for large global banks so far has been to capitalise on huge volumes of data that their siloed business units hold and are often constrained by manual processes, data duplication and legacy systems. The use cases for data and analytics in banking are endless. Massive data assets will mean that banks can more accurately gauge the risk of offering a loan to a customer. Banks are using data analytics to improve efficiency and increase productivity. Banks will be able to use their data to train machine learning (ML) algorithms that can automate many of their processes. Artificial Intelligence (AI) solutions have the potential to transform how banks deal with regulatory compliance issues, financial fraud and cybercrime. Banks will have to get better at using customer data for greater personalisation, enabling them to offer products and services tailored to individual consumers in real time. Today, banks have only just scratched the surface of data analytics. [...] For data analytics initiatives, banks now have the option of leveraging the best of open source technologies. Open source databases such as PostgreSQL, MongoDB and Apache Cassandra can deliver insights and handle any new source of data. With data models flexible enough for rich modern data, a distributed architecture built for cloud scale, and a robust ecosystem of tools, open source data platforms can help banks break free from data silos and enable them to scale their innovation.

  • Embedding computational thinking skills in our learning resources
  • Daniel Stenberg: Reducing mallocs for fun

    Everyone needs something fun to do in their spare time. And digging deep into curl internals is mighty fun! One of the things I do in curl every now and then is to run a few typical command lines and count how much memory is allocated and how many memory allocation calls that are made. This is good project hygiene and is a basic check that we didn’t accidentally slip in a malloc/free sequence in the transfer path or something. We have extensive memory checks for leaks etc in the test suite so I’m not worried about that. Those things we detect and fix immediately, even when the leaks occur in error paths – thanks to our fancy “torture tests” that do error injections. The amount of memory needed or number of mallocs used is more of a boiling frog problem. We add one now, then another months later and a third the following year. Each added malloc call is motivated within the scope of that particular change. But taken all together, does the pattern of memory use make sense? Can we make it better?

  • Daniel Stenberg: a Google grant for libcurl work

    Earlier this year I was the recipient of a monetary Google patch grant with the expressed purpose of improving security in libcurl. This was an upfront payout under this Google program describing itself as “an experimental program that rewards proactive security improvements to select open-source projects”. I accepted this grant for the curl project and I intend to keep working fiercely on securing curl. I recognize the importance of curl security as curl remains one of the most widely used software components in the world, and even one that is doing network data transfers which typically is a risky business. curl is responsible for a measurable share of all Internet transfers done over the Internet an average day. My job is to make sure those transfers are done as safe and secure as possible. It isn’t my only responsibility of course, as I have other tasks to attend to as well, but still.