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Showing posts with the label code

Imperial Measurement Converter App on Python

  Imperial measurement system converter app  I decided to spend an evening making a unit converter app without using an api to do the heavy lifting for me *and* with a python based gui because I haven't really made anything with one of them before and this is the result: πŸ‘πŸ‘πŸ‘      I installed Tinkter to get the GUI ( graphical user interface ) This is the GUI it produces when it's running.     The next job is to tidy up the code, it needs it.  Then I want to add some more functionality to it.  I can add volume conversion or weight .  I've always wanted to do some research into old money so I can add that to the app. After I've added a bit more functionality to it I'll find a way to host it on the public internet.     This is my advice to any other programmers out there:  Once you've got something working make the most of it because it's easier to add things onto something that's already working than startin...

Beginners Guide for Using Google Colab for the First Time.

Beginners guide for using the Machine Learning and/or AI tools on google.colab for a complete beginner:  AI and ML is all about analysing data so first you need to decide what kind of data you want to analyse. ( I refer you back to my previous blog post beginners guide to AI and ML terms. .) Basically do you want to analyse numerical data , text or images ? On this occasion I’m going to keep it simple and create some data visualisations  (graphs) You can get free open source data from this website kaggle.com I downloaded this file in .csv format: s-p-500-time-series-forecasting-with-prophet/input   Next find this website colab.research.google.com https://colab.research.google.com This is when you can go to chatgpt and ask it to write the code for google colab to analyse the data in  the way you want for example you could ask it to write the code to create a dataframe from that data:    Drag and drop the csv file into google.colab Change the labels s...

Advice For Beginners To Get Up and Running with Python

1. Why a Proper Setup Matters Before diving into code, a structured setup helps you avoid “spaghetti” projects that become hard to maintain. Using a virtual environment ensures your dependencies are isolated (so you don’t clash with system Python or other projects). Having a clear skeleton (separating imports, variables, functions, and “main program logic”) gives you and future readers a map of where each piece lives. Many software engineering experts emphasize that good architecture up front can save you enormous friction later — clean structure is one of the foundations of maintainability. ( Ciklum ) 2. Creating a Virtual Environment  πŸ’ΎπŸ’»πŸ’Ή Here’s a typical workflow: Open your terminal / command prompt in your project folder (or create a new folder). Run (for Python 3): python3 -m venv venv This creates a directory venv/ (or whatever you name it) containing the isolated Python environment. Activate it: On macOS / Linux: source venv/bin/activate On Windows ...

A Beginner’s Guide to AI & Machine Learning Terms (No Tech Background Needed!)

πŸ“ΆπŸ’»πŸ’Ή Artificial Intelligence (AI) and Machine Learning (ML) are terms that are used by a lot of people but do you know about the technical nuts and bolts of how they actually work? If you don't that's OK, — I’ve broken down some of the most common terms you might hear in the world of AI/ML into simple explanations anyone can understand. Plus, if you're using Google Colab , there's a built-in AI assistant called Gemini that can help you understand and write code but you need to know the right questions to ask it first.   1. NLP (Natural Language Processing) 🎀🎧 NLP is a branch of AI that helps computers understand and work with human language. Think of it as the technology behind things like chatbots, language translators, and voice assistants like Siri or Alexa. It allows machines to “read” and make sense of text or speech, just like we do. 2. BERT (Bidirectional Encoder Representations from Transformers) BERT is a special AI model developed by Google that he...

Time to Celebrate Success

  I Graduated with a 2:1 in Computing from Sheffield Hallam University! I'm incredibly proud to share that I’ve officially graduated from Sheffield Hallam University with a 2:1 in BSc Computing! It’s been a challenging, rewarding, and ultimately fulfilling journey, and I’m grateful for all the experiences, both technical and personal, that have shaped me along the way. From learning core computing principles to diving deep into specialist areas, my time at university has equipped me with the tools and confidence to move forward in the tech world. One of the standout modules for me was Data Management. I found it fascinating to explore how data is structured, stored, and retrieved, and how good design can make or break the efficiency of a system. This module really laid the groundwork for many of my later projects, giving me the practical skills and theoretical understanding to handle data effectively and responsibly—something that proved crucial in my final year work. My Artific...

Check Out Cuthbert's GitHub Pages site. https://cuthbert86.github.io/

This is a summary of my GitHub Pages site!   https://cuthbert86.github.io/ It is my personal corner of the web where I’m showcasing a range of projects and experiments as I learn more about web design and development. Whether you’re a fellow tech enthusiast, a curious visitor, or someone interested in learning alongside me, I hope you’ll find something interesting and useful here. The site is a living portfolio, featuring examples of my work as I explore different technologies and programming concepts. You’ll see projects built with HTML and enhanced with popular web tools like Bootstrap, giving my pages a clean and responsive look. I’m always updating and improving the design as I pick up new techniques, and many of the pages reflect my journey in web development. One of the highlights is my exploration of Python and its many applications. I’ve included projects that demonstrate data analysis, working with CircuitPython and MicroPython, and even connecting devices using MQTT....

Latest CV

  CUTHBERT BAINES PROFILE Full-stack python/Django developer. Intellectually curious and open minded Computing graduate specialising in Internet of Things (IoT) and microcomputing solutions. Experienced in working with microcontrollers, data collection, transmission, analysis, and live dashboards. Strong problem-solving skills and a proven work ethic from years in diverse roles. I am completely focused on completing the project at hand. Always do what needs to be done to make the project a success. Inquisitive mind, always willing to learn. Approachable and supportive of other Developers and Users.  Enjoys finding Innovative solutions to any problems that arise. ROLE I love solving problems. Looking for my first main role ideally in python / micro python / circuit python and Embedded software systems, data hubs, dashboards. I also have hardware experience. Plus experience in various languages and systems. So also very open to working with other languages and frameworks. KEY SK...

Micropython example

micropython code on github   This is the micropython code I used on my raspberry pico to collect sensor data and send it to my own home IOT mqtt broker.   I specialise in making code that can collect data and send it and perpetually continue to do that without getting an error disrupting the program.

Date-Time Fixer with Python

Sample of my Python Code https://github.com/cuthbert86/RiverProject/blob/main/datetime1.py This is an example of some of my data management work.  I wrote a python code that combined date and time columns into a single date-time column and then formatted it into .ISO format. import pandas as pd # Load your data from a CSV file (adjust the filename and path accordingly) file_path = 'riverdatacsv.csv' df = pd.read_csv(file_path) # Merge Date and Time columns into a single DateTime column with the ISO 8601 format df['DateTime'] = pd.to_datetime(df['Date'] + ' ' + df['Time']).dt.strftime('%Y-%m-%dT%H:%M:%S') # Drop the original Date and Time columns if needed df = df.drop(['Date', 'Time'], axis=1) # Display the DataFrame with the new DateTime column print(df)