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Python Async Function

5/5 – (1 vote)

As a Python developer, you might have encountered the terms async and await, wondering what they are and how to use them in your projects.

Async functions enable you to write concurrent code using the async/await syntax. This powerful duo allows you to perform multiple tasks simultaneously without blocking the execution of your code. ๐Ÿ›  Think of it as having multiple browser tabs open; while one page loads, you can continue browsing other tabs. This capability means your Python applications can become faster, more efficient, and capable of handling many I/O operations. ๐ŸŒŸ

To get started with async functions, you’ll need to get acquainted with Python’s asyncio library, which serves as a foundation for numerous asynchronous frameworks such as high-performance network and web servers, database connection libraries, and distributed task queues. ๐Ÿ”—

Mastering async functions can truly elevate your Python programming skills and help you build powerful and responsive applications. ๐Ÿ

Python Async Function Basics

First, you’ll learn the basics of Python async functions, which can help improve the performance of your asynchronous programming.

We’ll cover

  • async functions without await,
  • an async function example, async function return, and
  • async function call.

Async Function Without Await

You might wonder if it’s possible to create an async function without using the await keyword. Well, it is!

However, without await, the async function becomes somewhat less useful, since you won’t be able to pause its execution and yield control back to the event loop.

This means your async code will not be able to achieve cooperative concurrency, and other coroutines might be stuck waiting for their turn to execute. It’s generally a good idea to use await when working with async functions for more efficient asynchronous programming.

Async Function Example

Let’s dive into a simple example of using an async function in Python with asyncio:

import asyncio async def greet(name: str): print(f"Hello, {name}!") await asyncio.sleep(1) print(f"Nice to meet you, {name}!") async def main(): task1 = asyncio.create_task(greet("Alice")) task2 = asyncio.create_task(greet("Bob")) await task1 await task2 asyncio.run(main())

In this example, an async function greet is declared, which prints a greeting message, waits for 1 second using asyncio.sleep, and then prints another message.

The main asynchronous function creates two tasks to call greet with different names, running them concurrently.

Async Function Return

When you want to return a value from an async function, just use the return statement as you would in regular functions. However, keep in mind that the returned value will be wrapped in an asyncio.Future object, not the actual value.

You’ll need to use await to get the value when calling this async function.

For example:

async def calculate_result(): await asyncio.sleep(1) return "Result!" async def main(): result = await calculate_result() print(result) asyncio.run(main())

Here, calculate_result is an async function that returns a value after asynchronously waiting for 1 second. In the main() function, you can use await to get the actual value and print it. ๐ŸŒŸ

Async Function Call

To call an async function, you can’t simply use the normal function call syntax, because doing so would just return a coroutine object, not the actual result of the function. Instead, you have to use the await keyword to call the async function, or use asyncio.create_task or similar functions to run it concurrently:

# Using `await` to call async function
result = await async_function() # Using `asyncio.create_task` to run concurrently
task = asyncio.create_task(async_function())

Remember to always use the appropriate method to call your async functions in order to achieve efficient asynchronous programming with Python’s powerful async/await syntax.

Advanced Async Function Concepts

Next, you’ll explore advanced async function concepts to give you a better understanding of how they work in Python. ๐Ÿ˜Š Ready? Let’s dive in!

Async Function Decorator

To create an async function, you’ll use the async def syntax. This means you don’t have to use a decorator, but you can still decorate asynchronous functions with the @some_decorator syntax for better modularity in your programs.

For instance, consider using @asyncio.coroutine with a yield from syntax if you’re working with Python 3.4 or earlier. Or simply upgrade to newer versions! ๐Ÿ˜…

Async Function Type Hint

Type hints help improve the readability of your async code. Specify the input and output types of your async function using the typing module’s Coroutine and asyncio‘s Future objects.

Here’s an example:

from typing import Coroutine
import asyncio async def some_async_function() -> Coroutine[str]: await asyncio.sleep(1) return "done"

Async Function Returns Coroutine

An async function, also known as a coroutine, returns a coroutine object when called. You can use it as a direct call or pass it to an event loop to run the async function using asyncio.run() or loop.run_until_complete().

Keep in mind coroutine objects aren’t executed until you explicitly use an event loop or an await expression.

Async Function Await

When writing async functions, the await keyword is crucial. It allows you to pause the execution of a coroutine and wait for a result without blocking other coroutines.

You’ll often use await with I/O-bound operations, like reading from files, interacting with network services, or retrieving resources, which can take a significant amount of time.

โญ Recommended: Python __await()__ Magic Method

Async Function Type

An async function’s type is coroutine. So when defining your async function, you’re essentially creating a non-blocking function that allows other functions to run while it waits for results.

To check if an object in Python is a coroutine, you can use inspect.iscoroutine(obj) or inspect.iscoroutinefunction(obj).

Async Function Return Type

Async functions return a coroutine object, but you can also specify the type of the eventual returned value. For instance, if your async function performs some networking tasks and returns JSON data, you can specify the return type as Dict[str, Any].

Here’s how you do that:

from typing import Dict, Any, Coroutine
import asyncio async def fetch_json_data() -> Coroutine[Dict[str, Any]]: # some networking tasks here await asyncio.sleep(2) return {"key": "value"}

Async Function in Different Contexts

In this section, we will explore using async functions in different circumstances, such as within classes, threads, and converting async functions to sync. We will also discuss the concepts of async function sleep and handling functions that were never awaited.

Async Function in Class

When working with classes in Python, you might want to include asynchronous methods. To achieve this, just define your class method with async def. Remember to await your async methods when calling them to ensure proper execution.

Here’s an example:

class MyClass: async def my_async_method(self): await asyncio.sleep(1) async def main(): my_obj = MyClass() await my_obj.my_async_method()

Async Function in Thread

Running async functions in a thread can be tricky due to event loop requirements. Use asyncio.to_thread() for running async functions in threads. This will ensure your async function is executed within the correct thread’s event loop.

For example:

async def my_async_function(): await asyncio.sleep(1) print("Hello from async function!") async def main(): result = await asyncio.to_thread(my_async_function)

Async Function to Sync

If you need to call an async function from synchronous code, you can use asyncio.run() or create an event loop that runs a given coroutine.

Here’s an example of how to run an async function from sync code:

def sync_function(): asyncio.run(my_async_function())

Async Function Sleep

Sometimes, you might want to introduce a delay in your coroutine using asyncio.sleep. This allows other coroutines to run while waiting for IO operations or other events.

Example:

async def delayed_hello(): await asyncio.sleep(1) print("Hello after 1 second!")

๐Ÿ’ก Recommended: Time Delay in Python

Async Function Was Never Awaited

In some cases, you may forget to await an async function, which leads to warnings such as "coroutine 'my_async_function' was never awaited."

To prevent these issues, always ensure you’re using await when calling async functions:

async def my_async_function(): await asyncio.sleep(1) async def main(): # Missing 'await' would lead to a warning await my_async_function()

If you got something out of this article, I’m sure you’ll learn something of this one: ๐Ÿ‘‡

๐Ÿ’ก Recommended: Python Async With Statement โ€” Simplifying Asynchronous Code

I promise it has more beautiful pics. ๐Ÿ˜…

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11 Best ChatGPT Alternatives

5/5 – (1 vote)

If you’re looking for AI assistants that can help you with writing, coding, or even education, you’re in luck!

This article introduces 11 alternative and mostly open-source tools, each offering a unique set of features tailored to a different use case. By diving into these resources, you’ll discover new ways to make the most of AI in your everyday life. โœ๐Ÿค–๐Ÿ“š

Some noteworthy alternatives include Bard for writing, Codex for coding, and Duolingo Max for language learning.

LLaMA

Are you looking for a ChatGPT alternative? ๐Ÿ” Look no further than Meta’s LLaMA (Language Model Augmented Machine). Initially, LLaMA was designed as a text generator, but it also works well as a chatbot. It is a versatile language model pretrained on billions of texts ๐Ÿ“š from the internet and fine-tuned to provide accurate, intelligent responses. ๐Ÿ’ก

To use this alternative, you need intermediate-level programming skills, and it’s important to have the right hardware setup such as a powerful GPU. You can run a version of LLaMA, the LLaMa-13b model, on your local machine. For details on how to set it up, read this guide.

Regarding performance in natural language processing tasks, LLaMA-13b is highly capable ๐Ÿ†. It tries to produce content like poetry and stories, much like ChatGPT or OpenAI’s GPT-3 and GPT-4 models. How it behaves, though, depends on the specific tuning you apply during its setup. ๐Ÿ–ฅ

LLaMA supports multiple languages, providing a bilingual or multilingual experience for users. ๐ŸŒŽ However, if your focus is on a particular language, there are other ChatGPT alternatives you could consider like Jasper Chat.

Consider trying out LLaMA for your AI chatbot or text generation needs. Remember, though, it may require more coding and technical skills than some other alternatives.

๐Ÿ‘‰ Ressources: Read the paper “LLaMM: Open and Efficient Foundation Language Models” and check out the GitHub

Alpaca ๐Ÿฆ™

Yeah, the naming has gotten pretty obscure in the open-source large language model (LLM) community.

๐Ÿ’ก Short Summary: Instruction-following models like GPT-3.5 and ChatGPT are powerful but have flaws. Academia faces challenges in researching these models due to limited access.

Stanford researchers fine-tuned Alpaca, a language model based on Meta’s LLaMA 7B, using 52K instruction-following demonstrations from text-davinci-003.

Alpaca is small, easy to reproduce, and shows similar behaviors to text-davinci-003. The team is releasing their training recipe and data, with plans to release model weights and an interactive demo for academic research.

Commercial use is prohibited due to licensing restrictions and safety concerns.

Image credit

Alpaca is a remarkable chatbot alternative to ChatGPT that you can explore. Developed by Stanford researchers, it was fine-tuned using Facebook’s LLaMA to deliver impressive language capabilities ๐Ÿง ๐Ÿ’ฌ source.

This chatbot has the potential to enhance your SEO efforts, as it’s able to reason ๐Ÿค”, answer questions, and tell jokes, among other things. By integrating Alpaca into your online projects, you can engage users and attract search engine attention on platforms like Google and Bing ๐ŸŒ.

And it’s free and open-source! โญ

Here’s an example run from the original launch website:

There are also other open-source alternatives to ChatGPT that you may find useful, such as GPT4All, Dolly 2, and Vicuna ๐Ÿ’ป๐Ÿš€. You can find Python code to run these models on your system in this tutorial.

Vicuna ๐Ÿฆ™

Vicuna-13B, an open-source AI chatbot, is among the top ChatGPT alternatives available today. Its impressive feature parity with OpenAI’s ChatGPT and Google’s Bard has made it a popular choice for those seeking a capable language model.

๐Ÿ’ก Research Paper: “GPTQ: ACCURATE POST-TRAINING QUANTIZATION FOR GENERATIVE PRE-TRAINED TRANSFORMERS”

YouTube Video

What sets Vicuna apart is its ability to write code even though it is very concise and can run on your single-GPU machine (GitHub), which is less common in other open-source LLM chatbots ๐Ÿ’ป. This unique feature, along with its more than 90% quality rate, makes it stand out among ChatGPT alternatives.

๐Ÿ’ก Reference: Original Website

Don’t worry about compatibility, as Vicuna is available for use on your local machine or with cloud services like Microsoft’s Azure, ensuring you can access and collaborate on your writing projects wherever you are.

With Vicuna, you can expect the AI chatbot to deliver text completion tasks such as poetry, stories, and other content similar to what you would find on ChatGPT or Youchat. Thanks to its user-friendly interface and robust feature set, you’ll likely find this open-source alternative quite valuable.

OpenChatKit

๐Ÿš€ OpenChatKit is one of the ChatGPT alternatives worth exploring!

๐Ÿ’ก Official OpenChatKit Website (open-source)

Developed by Together Computer, OpenChatKit is an open-source variant of ChatGPT, providing users and developers the flexibility to tailor chatbot behavior to suit their specific needs.

You can use it online here in interactive mode — I tried it myself:

๐Ÿ›  If you’re a coder or developer, you’ll appreciate OpenChatKit’s compatibility with Python, allowing for seamless integration into your projects. You can also use it with GitHub Copilot to enhance your coding experience, thanks to the open-source nature of the project.

๐Ÿ’ก OpenChatKit caters to a broader audience since the open-source design allows access to users and groups that might not have the means to use proprietary models. It supports functions such as reasoning and multi-tasking, giving you an extensive range of applications to use within your projects.

It can also code: ๐Ÿ‘‡

GPT4ALL

GPT4ALL is an interesting alternative to ChatGPT that you might want to explore ๐Ÿ•ต.

It is a community-driven project aimed at offering similar capabilities to those of ChatGPT through the use of open-source resources ๐Ÿ”“.

The project is trained on a massive curated collection of written texts, which include assistant interactions, code, stories, descriptions, and multi-turn dialogues ๐Ÿ’ฌ (source).

๐Ÿ’ก Recommended: GPT4All Quickstart โ€“ Offline Chatbot on Your Computer

By choosing GPT4ALL as an alternative, you can access various resources such as:

  • Datasets
  • Model weights
  • Data curation processes

Getting Started with GPT4ALL

To get started, you’ll need to familiarize yourself with the project’s open-source code, model weights, and datasets. Information related to the project can be found through its project link provided in the search results.

Again, check out our detailed article on setting up GPT4ALL:

๐Ÿ’ก Recommended: GPT4All Quickstart โ€“ Offline Chatbot on Your Computer

What to Expect from GPT4ALL

While GPT4ALL may not match ChatGPT in terms of exact performance, it is a strong contender when it comes to providing a similar experience. Plus, it offloads some of the computational requirements associated with ChatGPT, possibly making it a more efficient option for you๐Ÿš€.

BlinkDL RWKV

Looking for a faster, more VRAM-efficient alternative to ChatGPT? ๐Ÿง Raven RWKV is an open-source chatbot that may just suit your needs! Developed by BlinkDL, this chatbot is powered by the RWKV language model which uses Recurrent Neural Networks (RNNs) ๐Ÿง  to achieve high-quality results similar to ChatGPT, but with better processing speed and lower hardware requirements ๐Ÿ’ช.

“RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it’s combining the best of RNN and transformer – great performance, fast inference, saves VRAM, fast training, “infinite” ctx_len, and free sentence embedding.” (source)

You’ll find that the Raven RWKV model has been fine-tuned on various datasets like Stanford Alpaca, code-alpaca, and more, offering an impressive performance with diverse conversation topics ๐Ÿค–. It has even managed to match the quality and scaling capabilities of transformer models, all while requiring less VRAM ๐Ÿ’พ.

You can check out an example run here:

If you’re eager to get started with Raven RWKV, head over to its Hugging Face repository for more details and relevant resources ๐Ÿ’ผ. Here, you’ll find different models like RWKV-4-Pile, which have been fine-tuned on diverse data sources to cater to various language preferences ๐ŸŒ.

In short, the Raven RWKV chatbot is an excellent choice if you seek to explore an open-source chatbot alternative that balances performance and resource consumption ๐Ÿš€.

StableLM (Stability-AI)

StableLM is an open-source alternative to ChatGPT developed by Stability AI. With its goal to provide a transparent and scalable AI language model, it’s perfect for anyone looking to explore options outside of proprietary tools. ๐ŸŒ

Based on a dataset called “The Pile,” StableLM aims to offer a powerful language-processing solution. You can access StableLM in its alpha form on GitHub with 3 billion and 7 billion parameter model options. This means you have the flexibility to choose the model that fits your project best. ๐Ÿ”ง

As you start working with StableLM, you’ll experience:

  • A solution that rivals ChatGPT in terms of performance and capabilities ๐Ÿ’ช
  • Alpha access, allowing you to get your hands on the technology early
  • A GitHub-based platform for easy use and integration with your projects

When trying it on the Hugging Face interface, however, I experienced that it’s still super slow. This is probably a problem of the server infrastructure though:

Remember, as with any open-source project, StableLM is ever-evolving. This means you have the opportunity to be a part of its development and contribute to the progress of the technology. ๐Ÿš€

KoboldAI

KoboldAI is a fantastic alternative to ChatGPT that you might want to try out ๐Ÿค–. It’s an open-source AI-powered chatbot capable of generating human-like text based on your prompts. You can find the source code and development updates on their GitHub repository.

Here’s an example run:

๐Ÿค– Bot : Hey!
💻 You : Hey Boyname, how have you been?
🤖 Bot : Been good! How about you?
💻 You : Been great to, excited to try out KoboldAI
🤖 Bot : KoboldAI is really fun!
💻 You : For sure! What is your favorite game?

When using KoboldAI, you’ll appreciate its easy-to-use interface that keeps things simple and clean ๐Ÿ’ป. Setting up the program may take a few steps, as you need to follow the instructions on their GitHub page. But worry not, they provide clear guidelines to make it easy for you ๐Ÿ”ง.

In fact, it’s one of the most comprehensive README pages on an AI GitHub repository I have ever seen. ๐Ÿ˜‰

The AI behind KoboldAI is driven by various pre-trained models, which give you options to find the perfect balance between performance and quality. You can experiment with different models to see which one fits your needs best ๐ŸŒŸ.

KoboldAI is focused on providing you with a chatbot experience that’s constantly improving. The active community of developers and users helps ensure that updates and bug fixes are addressed swiftly ๐Ÿ”ง.

Flan-T5-XXL

๐Ÿค– Meet Flan-T5-XXL, an impressive open-source language model that serves as an alternative to ChatGPT. Developed by Google, Flan-T5-XXL is an enhanced version of the T5 language model and excels in few-shot learning tasks.

๐ŸŒ Flan-T5-XXL benefits from instruction fine-tuning, a technique that improves model performance and generalization to unseen tasks. By leveraging this technique, you can expect better outcomes from Flan-T5-XXL compared to traditional language models.

๐Ÿ—ฃ This model stands out in applications involving multi-turn dialogues. Thanks to its Baize implementation, which mitigates potential risks by leveraging ChatGPT to facilitate conversations with itself, Flan-T5-XXL offers higher-quality results.

Usage

To get started with Flan-T5-XXL, you can access its model card for details on available features and implementation. Make sure to watch this YouTube video for code examples and optimization tips:

YouTube Video

And for further comparison with other language models, you can refer to this SourceForge comparison between ChatGPT, Flan-T5, and Visual ChatGPT.

๐Ÿ’ก Keep in mind that the open-source nature of Flan-T5-XXL offers a unique advantage because it is free to useโ€”unlike its proprietary counterpart, ChatGPT. To make an informed decision, compare the functionality of the Flan-T5-XXL and ChatGPT based on your specific needs.

For instance, I found that it’s not too advanced, GPT-4 would have figured this problem out: ๐Ÿ‘‡

By exploring Flan-T5-XXL as an alternative to ChatGPT, you can gain access to a powerful, open-source language model that offers diverse functionalities and improved performance. Happy experimenting! ๐Ÿš€

MiniGPT

MiniGPT is a lighter version of ChatGPT that you can use for various tasks. It’s perfect for those who need a simpler AI chatbot for straightforward tasks ๐Ÿ˜Š. In this section, you’ll discover a few alternatives to MiniGPT.

๐Ÿ’ก Recommended: MiniGPT-4: The Latest Breakthrough in Language Generation Technology

This neural network model has been developed to improve vision-language comprehension by incorporating a frozen visual encoder and a frozen large language model (LLM) with a single projection layer.

MiniGPT-4 has demonstrated numerous capabilities similar to GPT-4, like generating detailed image descriptions and creating websites from handwritten drafts.

One of the most impressive features of MiniGPT-4 is its computation efficiency. Despite its advanced capabilities, this model is designed to be lightweight and easy to use. This makes it an ideal choice for developers who need to generate natural language descriptions of images but donโ€™t want to spend hours training a complex neural network.

Image source: https://github.com/Vision-CAIR/MiniGPT-4

Additionally, MiniGPT-4 has been shown to have high generation reliability, meaning that it consistently produces accurate and relevant descriptions of images.

Google Bard

Google Bard is one of the top alternatives to ChatGPT you can try๐Ÿ’ก.

Built as Google’s response to ChatGPT, it utilizes a combination of two Language Models for Dialogue (LLMs) to create an engaging conversational experience (source). Offered by the search engine giant, you can expect some powerful AI capabilities from Bard. With Bard, you can enjoy a more dynamic way of searching for answers to various queries (source).

Image Credits: https://blog.google/technology/ai/bard-google-ai-search-updates/

To try Google Bard, visit the following link and sign up for access: Google Bard

Here are some key features of Google Bard:

  • It is an experimental AI conversational service, powered by Google’s LAMDA technology๐Ÿš€ (source).
  • Ideal for providing quick help with tasks like cover letter writing, event planning, and other needs you might have๐Ÿ“.(source).
  • Google Bard excels in precision and ease of use, making it a reliable alternative for those looking for a powerful AI chatbot.

Some More Resources

First, there’s GitHub Copilot, an LLM-based AI tool specifically designed to help coders increase their productivity.

Another remarkable choice is Character.AI, which focuses on natural language understanding and generation. It’s excellent for creating realistic conversations with virtual characters.

You should also consider YouChat as an alternative. It’s a powerful AI chatbot that helps you automate customer support and social media interactions.

Don’t miss Socratic๐ŸŽ“, as it’s designed to assist you with educational content. You can ask questions and receive detailed explanations in various subjects.

Next up is JasperChat. It’s a valuable tool for businesses looking to simplify their customer service and sales processes using AI chatbot technology.

Finally, WriteSonic is a worthy mention, especially if you need help with content writing tasks. It’s an AI-powered writing assistant that generates text for multiple purposes.

Okay, that’s it. Feel free to also download our free OpenAI cheat sheets such as this OpenAI Glossary:

OpenAI Glossary Cheat Sheet (100% Free PDF Download) ๐Ÿ‘‡

Finally, check out our free cheat sheet on OpenAI terminology, many Finxters have told me they love it! โ™ฅ

๐Ÿ’ก Recommended: OpenAI Terminology Cheat Sheet (Free Download PDF)

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Google Says โ€œWe Have No Moat, And Neither Does OpenAIโ€

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Key Points

  • The leaked document is titled “We Have No Moat, And Neither Does OpenAI.”
  • It argues that open-source AI development is winning and that Google and other companies have no competitive advantage or “moat” in the field.
  • The document suggests that Google and other companies should focus on building tools and infrastructure that support open-source AI development rather than trying to compete with it.
  • The document provides a fascinating insight into the state of AI development and the challenges facing companies like Google as they try to stay ahead of the curve.
  • Open-source development is unstoppable and has never been more alive! ๐Ÿฅณ

Diving Into the Document

A leaked Google document titled “We Have No Moat, And Neither Does OpenAI” has recently garnered attention. Shared anonymously on a public Discord server, the document comes from a Google researcher and offers a frank analysis of the AI development landscape.

The document contends that open-source AI development is prevailing, leaving Google and other companies without a competitive edge.

Considering Google’s status as an AI leader and its substantial investments, this is a notable claim.

๐Ÿ’ก Quote: “But the uncomfortable truth is, we arenโ€™t positioned to win this arms race and neither is OpenAI. While weโ€™ve been squabbling, a third faction has been quietly eating our lunch.”

Here are some interesting developments in the open-source community:

  • Offline Fast LLMs: As reported in a recent Finxter article, many large language models can now be run offline. A Twitter user even shared how he ran a foundation model on a Pixel 6 at 5 tokens per second speed!
  • Scalable Personal AI: Projects like Alpaca-Lora allow you to fine-tune a personalized AI on your notebook in a couple of hours.
  • Multimodality: Researchers release new multimodal models that are trained in less than one hour and are freely available via GitHub. Here‘s the paper.
  • Responsible Release: You can find a list of pre-trained LLMs for textual data generation on myriads of new websites. Other websites now share generative art models, generated by Midjourney or DALL-E, without restrictions. See an example here: ๐Ÿ‘‡

source

The researcher suggests that instead of competing with open-source AI, Google and other companies should concentrate on creating tools and infrastructure to support it. This strategy would ensure rapid AI advancements and widespread benefits.

Check out this wonderful analysis from the article:

๐Ÿ’ก Quote: “Many of the new ideas are from ordinary people. The barrier to entry for training and experimentation has dropped from the total output of a major research organization to one person, an evening, and a beefy laptop.”

The leak has sparked significant debate within the AI community, with some criticizing Google for not adequately supporting open-source AI and others lauding the company for recognizing its own limitations.

LoRA – An Innovation Worth Keeping In Mind

Low-Rank Adaptation of Large Language Models (LoRA) is a powerful technique we should focus on more.

LoRA works by simplifying model updates, making them much smaller and faster to process. This allows us to improve a language model quickly on regular computers, which is great for adding new and diverse information in real-time. Even though this technology could help Google’s ambitious projects, it’s not used enough.

Retraining models from scratch is difficult and time-consuming.

LoRA is effective because it can be combined with other improvements, like instruction tuning. These improvements can be added on top of each other to make the model better over time without needing to start from scratch.

This means that when new data or tasks become available, the model can be updated quickly and cheaply. On the other hand, starting from scratch wastes previous improvements and becomes very expensive.

We should think carefully about whether we need a new model for every new idea. If we have major improvements that make reusing old models impossible, we should still try to keep as much of the previous model’s abilities as possible.

I couldn’t resist adding this interesting quote from the article:

๐Ÿ’ก Quote: “LoRA updates are very cheap to produce (~$100) for the most popular model sizes. This means that almost anyone with an idea can generate one and distribute it. Training times under a day are the norm. At that pace, it doesnโ€™t take long before the cumulative effect of all of these fine-tunings overcomes starting off at a size disadvantage. Indeed, in terms of engineer-hours, the pace of improvement from these models vastly outstrips what we can do with our largest variants, and the best are already largely indistinguishable from ChatGPT. Focusing on maintaining some of the largest models on the planet actually puts us at a disadvantage.”

Timeline of LLM Developments (Overview)

Feb 24, 2023 – Meta launches LLaMA, an open-source code with various model sizes.

March 3, 2023 – LLaMA is leaked, allowing anyone to experiment with it.

March 12, 2023 – Artem Andreenko runs LLaMA on a Raspberry Pi.

March 13, 2023 – Stanford releases Alpaca, enabling low-cost fine-tuning of LLaMA.

March 18, 2023 – Georgi Gerganov runs LLaMA on a MacBook CPU using 4-bit quantization.

March 19, 2023 – Vicuna, a cross-university collaboration, achieves “parity” with Bard at $300 training cost.

March 25, 2023 – Nomic creates GPT4All, an ecosystem for models like Vicuna, at $100 training cost.

March 28, 2023 – Open Source GPT-3 by Cerebras outperforms existing GPT-3 clones.

March 28, 2023 – LLaMA-Adapter introduces instruction tuning and multimodality with just 1.2M learnable parameters.

April 3, 2023 – Berkeley launches Koala, users prefer it or have no preference 50% of the time compared to ChatGPT.

April 15, 2023 – Open Assistant launches a model and dataset for Alignment via RLHF, achieving near-ChatGPT human preference levels.

๐Ÿ’ก Recommended: 6 New AI Projects Based on LLMs and OpenAI

Competing with Open-Source is a Losing Game

I strongly believe in the power of open-source software development — we should build Bazaars not Cathedrals!

Open-source AI development is a better approach than closed-source AI development, particularly when considering the potential of Artificial General Intelligence (AGI). The open-source approach fosters collaboration, accessibility, and transparency, while promoting rapid development, preventing monopolies, and ensuring many benefits.

Here are a few reasons why I think open-source AI development should win in the long-term:

Collaboration is key in open-source AI, as researchers and developers from diverse backgrounds work together to innovate, increasing the likelihood of AGI breakthroughs.

Open-source AI is accessible to anyone, regardless of location or financial resources, which encourages a broader range of perspectives and expertise.

Transparency in open-source AI allows researchers to address biases and ethical concerns, fostering responsible AI development.

By building upon existing work, developers can rapidly advance AI technologies, bringing us closer to AGI.

Open-source AI also reduces the risk of single organizations dominating the AI landscape, ensuring that advancements serve the greater good.

Additionally, the benefits of AI are more evenly distributed across society through open-source AI, preventing the concentration of power and wealth.

Lastly, open-source AI development improves the security of AI systems, as potential flaws can be discovered and fixed by a larger community of researchers and developers.

Let’s end this article with another great quote from the article:

๐Ÿ’ก Quote: “Google and OpenAI have both gravitated defensively toward release patterns that allow them to retain tight control over how their models are used. But this control is a fiction. Anyone seeking to use LLMs for unsanctioned purposes can simply take their pick of the freely available models.”

Feel free to share this article with your friend โ™ฅ and download our OpenAI Python API Cheat Sheet and the following “Glossary” of modern AI terms:

OpenAI Glossary Cheat Sheet (100% Free PDF Download) ๐Ÿ‘‡

Finally, check out our free cheat sheet on OpenAI terminology, many Finxters have told me they love it! โ™ฅ

๐Ÿ’ก Recommended: OpenAI Terminology Cheat Sheet (Free Download PDF)

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30 Reasons Bitcoin Is Superior to Real Estate

5/5 – (1 vote)

Real estate is the largest asset class in the world. The market cap of all real estate is said to be north of $327 trillion (!) USD. Bitcoin, on the other hand, is tiny in comparison and trades at roughly $0.5 trillion USD at the time of writing.

However, many real estate investors buy real estate as a store of value to protect themselves from the inflation produced by the fiat system. However, billionaire investors such as Michael Saylor argue that Bitcoin is a far better and far more scarce store of value than real estate.

๐ŸŸ  There are billions of houses but only 21 million Bitcoin — let that sink in.

Store of Value Medium of Exchange Unit of Account
Bitcoin โญโญโญ โญโญโญ โญโญโญ
Real Estate โญโญ โญ โญ
  • Store of Value: Unlike real estate, Bitcoin is truly scarce — new houses can be built at will, but Bitcoin has a hard supply limit of 21 million.
  • Medium of Exchange: Bitcoin can be transferred easily whereas real estate is highly illiquid and has significant friction costs when sold.
  • Unit of Account: Bitcoin provides a full ledger system that has perfect predictability and, thereby, it is a much better unit of account than houses.

In this post, I’ll explore Bitcoin’s most crucial, specific advantages over real estate. ๐Ÿ‘‡

Disclaimer: While we consider every point on this list true, we are not financial advisors and this should not be considered financial advice. ๐Ÿ˜…

Reason #1 – Portability

Bitcoin can be easily transferred anywhere in the world, while real estate is limited to a specific location.

Reason #2 – Liquidity

Bitcoin can be bought and sold in an instant, while selling real estate can take months or even years.

Reason #3 – Accessibility

Bitcoin can be bought with a few clicks on a smartphone, while buying real estate requires significant capital and legal processes.

Reason #4 – Transparency

Bitcoin transactions are recorded on a public ledger, providing transparency and security, while real estate transactions can be opaque and subject to fraud.

Reason #5 – Security

Bitcoin is stored in digital wallets that can be easily secured with encryption, while real estate requires physical security measures. Nobody can take your Bitcoin without your cooperation. But many could take your real estate!

Reason #6 – Divisibility

Bitcoin can be divided into small fractions, making it more accessible to smaller investors, while real estate requires significant capital investment.

Reason #7 – Inflation hedge

Bitcoin has a limited supply, making it a good hedge against inflation, while real estate can be affected by inflation.

๐Ÿ’ก Recommended: The Bitcoin Valuation Thesis

Reason #8 – Low transaction fees

Bitcoin transactions have lower fees than real estate transactions, which can have high closing costs.

Reason #9 – No intermediaries

Bitcoin transactions can be done peer-to-peer without intermediaries, while real estate transactions require intermediaries such as real estate agents and lawyers.

Reason #10 – Accessibility to global markets

Bitcoin can be easily traded on global markets, while real estate investment is limited to local markets. For example, Bitcoin is accessible to Nigerian farmers who cannot invest in New York prime real estate.

Reason #11 – No maintenance costs

Bitcoin does not require maintenance costs, while real estate requires ongoing maintenance and repairs.

Reason #12 – No property taxes

Bitcoin does not require property taxes, while real estate is subject to property taxes.

Reason #13 – No zoning restrictions

Bitcoin is not subject to zoning restrictions, while real estate is subject to zoning regulations.

Reason #14 – No mortgage payments

Bitcoin does not require mortgage payments, while real estate requires ongoing mortgage payments.

Reason #15 – No tenant issues

Bitcoin does not require dealing with tenant issues, while real estate investment requires dealing with tenants and property management.

Reason #16 – No depreciation

Bitcoin does not depreciate in value, while real estate can depreciate over time.

Reason #17 – No insurance costs

Bitcoin does not require insurance costs, while real estate requires insurance coverage.

Reason #18 – No legal disputes

Bitcoin transactions are irreversible, reducing the likelihood of legal disputes, while real estate transactions can be subject to legal disputes, such as property disputes and contract disputes.

Reason #19 – No inspection costs

Bitcoin does not require inspection costs, while real estate requires inspection costs before purchase.

Reason #20 – No closing costs

Bitcoin transactions do not require closing costs, while real estate transactions can have significant closing costs.

Reason #21 – No need for physical presence

Bitcoin can be bought and sold remotely, while real estate transactions often require physical presence.

Reason #22 – No need for financing

Bitcoin can be bought with cash, while real estate often requires financing through loans.

Reason #23 – No need for credit checks

Bitcoin transactions do not require credit checks, while real estate transactions often require credit checks for financing.

Reason #24 – No need for appraisals

Bitcoin transactions do not require appraisals, while real estate transactions often require appraisals before purchase.

Reason #25 – No need for property inspections

Bitcoin transactions do not require property inspections, while real estate transactions often require property inspections before purchase.

Reason #26 – No natural disaster risks

Bitcoin is not subject to natural disaster risks, while real estate can be affected by natural disasters such as floods and earthquakes.

Reason #27 – No environmental risks

Bitcoin does not have environmental risks, while real estate can be affected by environmental hazards such as pollution and toxic waste.

Reason #28 – No eminent domain risks

Bitcoin is not subject to eminent domain risks, while real estate can be subject to government seizure for public use.

Reason #29 – No zoning changes

Bitcoin is not subject to zoning changes, while real estate can be affected by changes in zoning regulations.

Reason #30 – No need for property management

Bitcoin does not require property management, while real estate investment requires property management and maintenance.

๐Ÿ’ก Recommended: 11 Best Bitcoin Books: Your Ultimate Guide for 2023

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11 Best Bitcoin Books: Your Ultimate Guide for 2023

5/5 – (1 vote)

Bitcoin is still the king and number one crypto asset and a revolutionary decentralized technology that will outlast all of us and all of our institutions.

One of the best ways to gain insight into the world of bitcoin and cryptocurrencies is by reading books on the subject. These books provide invaluable information, covering everything from the history and mechanics of Bitcoin to investment strategies and future prospects.

I’ve spent countless hours researching and comparing various bitcoin books to help you find the best options that cater to a range of interests and knowledge levels.

๐Ÿ’ก No affiliate or sponsored links on this page! So you can trust that the book recommendations are genuine. ๐Ÿ˜Š

So, let’s get started with my top picks — the last book on the list should be much more popular in my opinion! I love it! The first two, however, are my top pics! ๐Ÿ‘‡

Softwar: A Novel Theory on Bitcoin

This book is a must-read for those who want to broaden their perspective on the strategic significance of Bitcoin.

Pros

  • Comprehensive insights on Bitcoin’s future potentials
  • Engaging writing style
  • Carefully structured chapters

Cons

  • Some readers might find parts hard to digest
  • Not suitable for casual reading
  • May require background knowledge

In “Softwar: A Novel Theory on Power Projection and the National Strategic Significance of Bitcoin,” author Jason Lowery combines an engaging writing style with a wealth of thought-provoking insights. As you delve into the content, you’ll appreciate how the chapters build upon themselves to help you better understand Bitcoin’s potential impact on the world stage.

The depth and breadth of the topics covered in this book are truly impressive, as they offer a rare glimpse into the diverse possibilities and implications of Bitcoin’s future development. While reading, you can’t help but feel eager to explore more about this rapidly evolving digital currency.

However, it’s worth noting that “Softwar” may not be for everyone. Some sections may require extra effort to fully digest, and a casual reader might find it challenging to keep up with the dense material. Additionally, the book assumes a certain level of background knowledge about Bitcoin, which might make it harder for newcomers to the subject.

๐Ÿ’ก “Softwar: A Novel Theory on Power Projection and the National Strategic Significance of Bitcoin” is an enlightening read for those who truly want to understand the wider implications of this revolutionary digital asset. If you’re prepared to commit time and effort to gain a deeper understanding of Bitcoin’s potential, this book will undoubtedly expand your horizons.

The Bitcoin Standard

This is a must-read for those seeking to learn about bitcoin’s potential to transform traditional financial systems. Evidently, The Bitcoin Standard has “orange-pilled” more people than any other book! ๐ŸŸ 

Pros

  • Comprehensive understanding of bitcoin and its potential
  • Intriguing historical context of money and economics
  • Easy to comprehend language for different audiences

Cons

  • May contain some factual inaccuracies
  • Can seem overly opinionated at times
  • Oversimplification of complex monetary systems

The Bitcoin Standard comes off as an insightful resource for understanding the concept of Bitcoin as a decentralized alternative to central banking. The first few paragraphs give a thorough overview of the technology behind Bitcoin, helping you grasp its significance and potential to revolutionize the financial world.

In addition to exploring Bitcoin’s technological aspects, the book delves into the history of money and economics, enhancing your overall knowledge of the subject matter. It allows you to see how Bitcoin fits into the broader narrative of the evolution of money, making connections you never knew existed.

However, it’s important to note that some readers have pointed out minor factual inaccuracies in the book. While these don’t completely undermine the book’s overall message and importance, it’s a good idea to approach it with a discerning mindset, cross-checking facts along the way.

Another hurdle for some readers might be the author’s pronounced opinions about certain topics. Although this adds character to the book, it can occasionally make the content seem one-sided, taking away from the reader’s ability to form their own opinion on Bitcoin and its potential.

Lastly, the book has received criticism for its oversimplification of complex monetary systems. While this can make it more digestible for readers with limited knowledge of the financial world, it can sometimes lead to misunderstandings or misconceptions about certain aspects of money and economics.

๐Ÿ’ก The Bitcoin Standard is an engaging and informative read for anyone interested in understanding the world of Bitcoin and its potential to disrupt traditional financial systems. Despite its drawbacks, the book offers valuable insights into the history of money and the role Bitcoin could play in the future of finance.

Bitcoin and Beef: Criticisms, Similarities, and Why Decentralization Matters

This book offers fascinating insight into the interconnectedness of Bitcoin and food systems, making it a compelling read.

Pros

  • Provides valuable information on Bitcoin and the food industry
  • Easy to understand for beginners
  • Reveals the importance of decentralization

Cons

  • Might lack depth for advanced readers
  • Could be seen as connecting unrelated topics
  • Some readers may find the writing style amateurish

“Bitcoin and Beef: Criticisms, Similarities, and Why Decentralization Matters” is an intriguing read that bridges the gap between cryptocurrency and food systems. You’ll find yourself drawn into the world of Bitcoin and beef, learning how they share similarities and why decentralization is essential in both areas.

As you dive into the book, you’ll appreciate the way it simplifies complex concepts, making them accessible even if you’re a novice in these topics. The author does an excellent job presenting factual information while keeping the text engaging and easy to follow.

However, if you’re already well-versed in Bitcoin or the food industry, you might find the book lacking depth in certain areas. Additionally, some readers might feel that connecting Bitcoin and beef is a stretch, making the content appear somewhat disjointed.

That being said, this book is undoubtedly an enlightening read for anyone interested in both the economic and social aspects of Bitcoin and food systems. It combines an easy-to-read style with valuable information that will challenge your preconceptions about these two seemingly unrelated topics.

๐Ÿ’ก “Bitcoin and Beef” is a fascinating journey into the world of decentralization that will leave you eager to explore more.

The Basics of Bitcoins and Blockchains

Dive into the world of cryptocurrencies with confidence through this comprehensive and easy-to-understand guide.

Pros

  • Comprehensive and clear introduction
  • Engaging and informative writing style
  • Covers history, technology, and practical uses

Cons

  • May seem lengthy for some readers
  • Images are occasionally difficult to read
  • Lacks an index

As you delve into the pages of “The Basics of Bitcoins and Blockchains,” you’ll find yourself captivated by the approachable and informative writing style that brings clarity to the often complex topics of cryptocurrencies and blockchain technology.

Whether you’re a beginner or already have some knowledge about the subject, this book serves as an excellent resource that will undoubtedly help you grasp essential concepts with ease.

However, it is not for Bitcoin maxis! ๐Ÿ˜…

The book provides a solid foundation by explaining the history of banking systems and money, which sets the stage for understanding the value and importance of cryptocurrencies in today’s digital world. The author demonstrates expertise in the subject matter while maintaining simplicity and excitement, ensuring that you remain engaged throughout your reading journey.

However, some readers might find the book to be slightly lengthy or find certain images harder to read. Additionally, the lack of an index might be a drawback for those wanting quick reference points. Nevertheless, “The Basics of Bitcoins and Blockchains” merits its 4.5-star rating and its position as an excellent introduction to the world of cryptocurrencies.

๐Ÿ’ก If you’re eager to learn about the future of money and the technology driving it, “The Basics of Bitcoins and Blockchains” is a must-read that will leave you more knowledgeable and ready to embrace the world of digital assets.

Catching Up to Crypto: Your Guide to Bitcoin and the New Digital Economy

This comprehensive yet approachable guide is perfect for those looking to explore and understand the world of Bitcoin and its impact on the digital economy.

Pros

  • In-depth introduction to Bitcoin and cryptocurrency
  • Easy-to-understand explanations for beginners
  • Insightful anecdotes and industry history

Cons

  • Might be too basic for advanced users
  • Limited focus on alternative cryptocurrencies
  • No specific investment advice offered

Diving into “Catching Up to Crypto,” you’ll find this book eases your transition into the sometimes intimidating world of cryptocurrency. The author methodically breaks down complex concepts into understandable terms, accompanied by engaging anecdotes and a well-structured history of the industry.

As a beginner, you’ll have an enjoyable and enlightening experience as you explore the fascinating world of digital currencies.

While some prior knowledge of cryptocurrency may be helpful, it’s not essential, as the book covers nearly every aspect you need to understand. From the birth of Bitcoin to the potential future developments in the new digital economy, this guide serves as a comprehensive starting point.

The book delivers the necessary knowledge to participate in conversations and make informed decisions regarding your ventures into the crypto sphere.

However, it’s essential to consider that “Catching Up to Crypto” focuses primarily on Bitcoin and its role in shaping the modern financial landscape.

This may be perfect for newcomers, but experienced investors seeking in-depth information on alternative cryptocurrencies may require additional resources. Although the book provides a wealth of knowledge, direct investment advice is not offered.

๐Ÿ’ก If you’re seeking to educate yourself on the basics of Bitcoin and its influence on our digital economy, “Catching Up to Crypto” provides an engaging and accessible introduction. This book will leave you feeling confident about your ability to navigate this rapidly evolving space.

The Only Cryptocurrency Investing Book You’ll Ever Need

This beginner-friendly guide makes understanding and investing in cryptocurrency achievable for anyone seeking to enter the market.

Pros

  • Comprehensive yet easy-to-understand
  • Addresses various aspects of the crypto market
  • Credible author with real-world experience

Cons

  • Limited focus on practical applications of cryptocurrencies
  • Might be too basic for seasoned investors
  • May not cover all aspects of cryptocurrency investing

As a newcomer to the cryptocurrency world, you will benefit immensely from “The Only Cryptocurrency Investing Book You’ll Ever Need.”

The author does a fantastic job of breaking down complex concepts into digestible pieces of information, providing readers with a solid foundation of knowledge on the subject. From learning about the blockchain technology to understanding various digital assets’ value, this guide takes you through the important aspects of the ever-growing crypto space.

Again, this is not for Bitcoin maxis! ๐Ÿ˜…

One particular advantage of this book is its ability to cater to absolute beginners. The author assumes no prior knowledge of the subject and explains concepts in simple, easy-to-understand language. As you progress through the pages, you’ll gain confidence and insights into how the cryptocurrency market operates, and be better prepared to make informed investment decisions.

However, as informative and engaging as this book is, it may not be suitable for seasoned investors or those with a deeper understanding of cryptocurrencies. The content may come across as too basic for some, but it still serves as a great refresher for anyone looking to brush up on their cryptocurrency knowledge.

๐Ÿ’ก “The Only Cryptocurrency Investing Book You’ll Ever Need” is an excellent introductory guide for beginners looking to understand and invest in the rapidly growing world of cryptocurrencies. While it may not cover every nuance of the market, it provides a strong foundation for further exploration and learning, setting you on the path to becoming a successful crypto investor.

Crypto for Beginners: A Simple Non-Technical Guide

An interesting read for crypto beginners who want a simple, non-technical guide to grasp the blockchain revolution and make a wise investment in cryptocurrency.

Pros

  • Easy to understand language
  • Covers blockchain and crypto investing basics
  • Helpful for creating multi-generational wealth

Cons

  • Might not cater to advanced crypto investors
  • Limited to 204 pages of content
  • Independent publication, not by a major publisher

Crypto for Beginners promises a non-technical guide to understanding the blockchain revolution and bolstering your crypto investing knowledge.

The book is written in simple, approachable language that makes it easy for you to grasp the concepts without getting bogged down in technical jargon. The author does a great job of breaking down the history and fundamental aspects of cryptocurrencies, allowing you to make informed decisions about your investments.

Despite being only 204 pages long, this book covers a wide range of topics, including the history of money, the technology behind cryptocurrencies, and practical tips for getting started in crypto investing. The content is engaging and will help you build the foundation you need to create multi-generational wealth through smart cryptocurrency investments.

However, if you’re already an experienced crypto investor, this book might not offer much new or advanced information.

Additionally, the book is published independently, which may raise some concerns about the credibility of the information for some readers.

Nevertheless, with a high rating of 4.8 stars and numerous positive reviews, it is clear that this guide has resonated with many beginners looking to enter the world of cryptocurrency investing.

๐Ÿ’ก Crypto for Beginners is a valuable resource for those new to the crypto space or those who want a simplified guide to the blockchain revolution. Although it may not address the needs of advanced investors, it’s worth considering, especially if you’re keen on creating a well-rounded, long-term investment strategy in cryptocurrency.

Blockchain Bubble or Revolution

A must-read for anyone looking to dive into the world of cryptocurrencies and blockchain technology.

Pros

  • Easy-to-read for non-technical readers
  • Balanced and unbiased approach
  • Informative with clear examples

Cons

  • Might be too basic for experts
  • Limited in-depth technical coverage
  • Could benefit from more in-depth case studies

Diving into the world of blockchain and cryptocurrencies can be intimidating, but “Blockchain Bubble or Revolution” manages to make the learning process engaging and accessible. You’ll find that the book breaks down complex concepts into digestible information, which is perfect if you are a beginner or someone without a technical background.

This book exceptionally presents a balanced and unbiased view of blockchain technology, truly exploring its potential while remaining cautious about over-hyped claims. You’ll appreciate the authors’ honesty as they share the pros and cons of using blockchain, and you’ll better understand its place in our modern world.

One downside might be that if you are already quite familiar with blockchain technology, you could find some portions of the book too basic. The book also might leave you wanting more when it comes to detailed technical explanations or in-depth case studies. However, this should not deter you if you are simply looking to familiarize yourself with the fundamentals of blockchain.

๐Ÿ’ก “Blockchain Bubble or Revolution” is an excellent starting point for anyone looking to learn about the future of Bitcoin, blockchains, and cryptocurrencies. The combination of non-technical language, engaging writing style, and a balanced perspective make it a must-read in your journey to understand this revolutionary technology.

A Beginner’s Guide To Bitcoin

A concise and informative read to help you kickstart your journey into the world of Bitcoin.

Pros

  • Easy to understand
  • Covers fundamentals and protection tips
  • Written by a reputable author who is also a Bitcoin maxi

Cons

  • Limited to 68 pages
  • Geared towards a US audience
  • Might be too basic for those with some Bitcoin knowledge

As a newcomer to the cryptocurrency scene, you’ll find that “A Beginner’s Guide To Bitcoin” simplifies complex concepts and makes them palatable for you.

The author, Matthew Kratter, has created an engaging guide that covers fundamental information about Bitcoin and how to protect your investment. Trust me, I benefited a lot from this knowledge, especially his YouTube channel “Bitcoin University”. โญ

Although the guide is concise and easy to finish within just a couple of sittings, it’s primarily aimed at a US audience, which might not address some concerns for international readers. However, if you seek clarity and guidance, then this book is worth giving a shot.

๐Ÿ’ก “A Beginner’s Guide To Bitcoin” is an excellent starting point for anyone who wants to dip their toes into the world of cryptocurrency. By the time you turn the last page, you’ll be equipped with the essential knowledge and confidence to navigate the often-confusing world of Bitcoin. Happy investing!

Bitcoin Clarity: The Complete Beginners Guide to Understanding

A highly recommended read if you want to dive into the world of Bitcoin and understand its essence.

Pros

  • Easy-to-understand language
  • Comprehensive coverage of Bitcoin concepts
  • Engaging explanations and visuals

Cons

  • Some reported typos
  • Might be a bit technical for complete beginners
  • Physical book suggested for easier reference

Diving into the complex world of Bitcoin, you’ll find that “Bitcoin Clarity” makes the subject approachable and engaging. Written by Kiara Bickers, this audiobook not only provides high-level fundamentals but also helps you make informed decisions when purchasing Bitcoin.

Throughout your journey with this book, you’ll appreciate the variety of examples and visuals that the author employs to break down technical details. The book is aimed at beginners, but some readers might find it helpful to start with a basic understanding of digital currencies, as the content may lean towards the technical side.

One suggestion from readers is to get the hard copy of the book, which makes it easier to reference back to earlier chapters. There have also been mentions of a few typos in the book, but this does not take away from the valuable information it offers.

๐Ÿ’ก “Bitcoin Clarity” offers a thorough and engaging entrance into the world of digital currency. If you’re interested in understanding Bitcoin’s technical and practical aspects, this book will serve as a valuable resource.

Bitcoin: Hard Money You Can’t F*ck With: Why Bitcoin Will Be the Next Global Reserve Currency

“Bitcoin: Hard Money You Can’t F*ck With: Why Bitcoin Will Be the Next Global Reserve Currency” by Jason A. Williams is an insightful and thought-provoking book that explores the potential of Bitcoin as a global reserve currency.

โญ The author argues that Bitcoin is “hard money” that cannot be manipulated by governments, central banks, or corporations, making it a safe and secure store of value.

The book provides a comprehensive overview of Bitcoin, including its history, technology, and potential uses. The author explains how Bitcoin emerged out of the 2008 banking crisis, and why money printing slowly destroys wealth. The book also covers the basics of money and currency, and how printing cash has historically led to the death of currency.

One of the strengths of the book is the author’s ability to explain complex concepts in a clear and concise manner. The book is accessible to readers with little or no prior knowledge of Bitcoin, and provides a great introduction to the topic. The author also includes practical advice on how to invest in Bitcoin, and how to store it securely.

๐Ÿ’ก “Bitcoin: Hard Money You Can’t F*ck With” is a must-read for anyone interested in the future of money and currency. The book is well-researched, engaging, and provides a compelling argument for why Bitcoin could become the next global reserve currency. I highly recommend this book to anyone looking to learn more about Bitcoin and its potential impact on the world.

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How I Designed a Personal Portfolio Website with Django (Part 2)

5/5 – (1 vote)

We are designing a personal portfolio website using the Django web framework.

In the first part of this series, we learned how to create Django models for our database. We created an admin panel, and added ourselves as a superuser. I expect that by now you have used the admin panel to add your sample projects.

You are required to go through the first part of this series for you to follow along with us if you haven’t already done so. In this series, we will create a view function using the sample projects. By the end of this series, we will have created a fully functioning personal portfolio website.

The View Function

We can choose to use class-based views or function-based views, or both to create our portfolio website.

If you use class-based views, you must subclass it with Django’s ListView class to list all your sample projects. For a full sample project description, you must create another class and subclass it with Django’s DetailView class.

For our portfolio website, we will use function-based views so that you can learn how to query our database. Open the views.py file and write the following code:

from django.shortcuts import render
from .models import Project # Create your views here. def project_list(request): projects = Project.objects.all() context = { 'projects': projects } return render(request, 'index.html', context)

We import the Projects class and perform a query to retrieve all the objects in the table. This is a simple example of querying a database.

The context dictionary that contains the object is sent to the template file using the render() function. The function also renders the template, index.html. This tells us that all information available in the context dictionary will be displayed in the given template file.

This view function will only list all our sample projects. For a full description of the projects, we will have to create another view function.

def full_view(request, pk): project = Project.objects.get(pk=pk) context = { 'project': project } return render(request, 'full_view.html', context)

This view function looks similar to the previous one only that it comes with another parameter, pk. We perform another query to get an object based on its primary key. I will soon explain what is meant by primary key.

Template Files

We have to create two template files for our view functions. Then, a base.html file with Bootstrap added to make it look nice. The template files will inherit everything in the base.html file. Copy the following code and save it inside the templates folder as base.html.

<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.1.3/css/bootstrap.min.css" integrity="sha384-MCw98/SFnGE8fJT3GXwEOngsV7Zt27NXFoaoApmYm81iuXoPkFOJwJ8ERdknLPMO" crossorigin="anonymous"> <nav class="navbar navbar-expand-lg navbar-light bg-light"> <div class="container"> <a class="navbar-brand" href="{% url 'index' %}">Personal Portfolio</a> <button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbarSupportedContent" aria-controls="navbarSupportedContent" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="collapse navbar-collapse" id="navbarSupportedContent"> <ul class="navbar-nav mr-auto"> <li class="nav-item active"> <a class="nav-link" href="{% url 'index' %}">Home</a> </li> </ul> </div> </div> </nav> <div class="container"> {% block content %}{% endblock %}
</div> <script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.3/umd/popper.min.js" integrity="sha384-ZMP7rVo3mIykV+2+9J3UJ46jBk0WLaUAdn689aCwoqbBJiSnjAK/l8WvCWPIPm49" crossorigin="anonymous"></script>
<script src="https://stackpath.bootstrapcdn.com/bootstrap/4.1.3/js/bootstrap.min.js" integrity="sha384-ChfqqxuZUCnJSK3+MXmPNIyE6ZbWh2IMqE241rYiqJxyMiZ6OW/JmZQ5stwEULTy" crossorigin="anonymous"></script>

The a tag has a {% ... %} tag in its href attribute. This is Django’s way of linking to the index.html file. The block content and endblock tags inside the div tag is reserved for any template file inheriting from the base.html file.

As we will see, any template file inheriting from the base.html file must include such tags.

The Bootstrap files are beyond the scope of this article. Check the documentation to learn more.

Create the index.html file inside the template folder as indicated in the view function. Then, write the following script:

{% extends "base.html" %}
{% load static %}
{% block content %}
<h1>Projects Completed</h1>
<div class="row">
{% for project in projects %} <div class="col-md-4"> <div class="card mb-2"> <img class="card-img-top" src="{{ project.image.url }}"> <div class="card-body"> <h5 class="card-title">{{ project.title }}</h5> <p class="card-text">{{ project.description }}</p> <a href="{% url 'full_view' project.pk %}" class="btn btn-primary"> Read More </a> </div> </div> </div> {% endfor %}
</div>
{% endblock %}

The index.html file inherits the base.html as shown in the first line of the code. Imagine all the scripts we have to write to render hundreds or even thousands of sample projects! Once again, Django comes to the rescue with its template engine, for loops.

Using the for loop, we loop through all the projects (no matter how many they are) passed by the context dictionary. Each iteration renders the image (using the img tag), the title, the description and a link to get the full description of the project.

Notice that the a tag is pointing to a given project represented as project.pk. This is the primary key passed as a parameter in the second view function. More on that soon.

Again, notice the block content and the endblock tags. Since the index.html extends the base.html file, it will only display all that is found in the file. Any addition must be written inside the block content and endblock tags. Otherwise, it won’t be displayed.

Finally, the full_view.html file:

{% extends "base.html" %}
{% load static %}
{% block content %}
<h1>{{ project.title }}</h1>
<div class="row"> <div class="col-md-8"> <img src="{% project.image.url %}" alt="" width="100%"> </div> <div class="col-md-4"> <h5>About the project:</h5> <p>{{ project.description }}</p> <br> <h5>Technology used:</h5> <p>{{ project.technology }}</p> </div>
</div>
{% endblock %}

Thatโ€™s all about our template files. Notice how everything is linked to our database model as we learned in the first part of this series. We retrieve data from the database, pass them to the view function, and render them in the template files.

We havenโ€™t let Django know of an existing templates folder. Go to the settings.py file, under the TEMPLATES section. Register the templates folder there.

TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates')], #add these 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, },
]

Registering the URLs

We need to hook up our view functions to URLs. First is the project-level URL. Open the urls.py in the project folder, and add these:

from django.contrib import admin
from django.urls import path, include
from django.conf import settings
from django.conf.urls.static import static urlpatterns = [ path('admin/', admin.site.urls), path('', include('portfolio.urls')),
] if settings.DEBUG: urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

We register the file using the include() method. So, once we start the local server, and go to http://127.0.0.1:8000, we will see our home page which is the index.html file. Then, in the if statement, we tell Django where to find the user-uploaded images.

Next are the app-level URLs. Create a urls.py file in the portfolio folder.

from django.urls import path
from .views import project_list, full_view urlpatterns = [ path('', project_list, name='index'), path('<int:pk>/', full_view, name='full_view'),
]

The full_view URL is hooked up with a primary key. It is the same primary key in the templates file and in the second view function. This is an integer representing the number of each project. For the first project you added in the previous series, the URL will be http://127.0.0.1:8000/1

Hoping that everything is set, start the local server, and you will see everything displayed.

Conclusion

We have successfully come to the end of the second series of this project. The first series is here:

๐Ÿ’ก First Part: How I Designed a Personal Portfolio Website with Django

If you encounter errors, be sure to check the full code on my GitHub page. Our app is now running in the local server. In the final series of this project, we will see how we can deploy this to a production server.

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Python Converting List of Strings to * [Ultimate Guide]

5/5 – (1 vote)

Since I frequently handle textual data with Python ๐Ÿ, I’ve encountered the challenge of converting lists of strings into different data types time and again. This article, originally penned for my own reference, decisively tackles this issue and might just prove useful for you too!

Let’s get started! ๐Ÿ‘‡

Python Convert List of Strings to Ints

This section is for you if you have a list of strings representing numbers and want to convert them to integers.

The first approach is using a for loop to iterate through the list and convert each string to an integer using the int() function.

Here’s a code snippet to help you understand:

string_list = ['1', '2', '3']
int_list = [] for item in string_list: int_list.append(int(item)) print(int_list) # Output: [1, 2, 3]

Another popular method is using list comprehension. It’s a more concise way of achieving the same result as the for loop method.

Here’s an example:

string_list = ['1', '2', '3']
int_list = [int(item) for item in string_list]
print(int_list) # Output: [1, 2, 3]

You can also use the built-in map() function, which applies a specified function (in this case, int()) to each item in the input list. Just make sure to convert the result back to a list using list().

Take a look at this example:

string_list = ['1', '2', '3']
int_list = list(map(int, string_list))
print(int_list) # Output: [1, 2, 3]

For a full guide on the matter, check out our blog tutorial:

๐Ÿ’ก Recommended: How to Convert a String List to an Integer List in Python

Python Convert List Of Strings To Floats

If you want to convert a list of strings to floats in Python, you’ve come to the right place. Next, let’s explore a few different ways you can achieve this. ๐Ÿ˜„

First, one simple and Pythonic way to convert a list of strings to a list of floats is by using list comprehension.

Here’s how you can do it:

strings = ["1.2", "2.3", "3.4"]
floats = [float(x) for x in strings]

In this example, the list comprehension iterates over each element in the strings list, converting each element to a float using the built-in float() function. ๐Ÿ”„

Another approach is to use the map() function along with float() to achieve the same result:

strings = ["1.2", "2.3", "3.4"]
floats = list(map(float, strings))

The map() function applies the float() function to each element in the strings list, and then we convert the result back to a list using the list() function. ๐Ÿ—บ

If your strings contain decimal separators other than the dot (.), like a comma (,), you need to replace them first before converting to floats:

strings = ["1,2", "2,3", "3,4"]
floats = [float(x.replace(',', '.')) for x in strings]

This will ensure that the values are correctly converted to float numbers. ๐Ÿ”ข

๐Ÿ’ก Recommended: How to Convert a String List to a Float List in Python

Python Convert List Of Strings To String

You might need to convert a list of strings into a single string in Python. ๐Ÿ˜„ It’s quite simple! You can use the join() method to combine the elements of your list.

Here’s a quick example:

string_list = ['hello', 'world']
result = ''.join(string_list) # Output: 'helloworld'

You might want to separate the elements with a specific character or pattern, like spaces or commas. Just modify the string used in the join() method:

result_with_spaces = ' '.join(string_list) # Output: 'hello world'
result_with_commas = ', '.join(string_list) # Output: 'hello, world'

If your list contains non-string elements such as integers or floats, you’ll need to convert them to strings first using a list comprehension or a map() function:

integer_list = [1, 2, 3] # Using list comprehension
str_list = [str(x) for x in integer_list]
result = ','.join(str_list) # Output: '1,2,3' # Using map function
str_list = map(str, integer_list)
result = ','.join(str_list) # Output: '1,2,3'

Play around with different separators and methods to find the best suits your needs.

Python Convert List Of Strings To One String

Are you looking for a simple way to convert a list of strings to a single string in Python?

The easiest method to combine a list of strings into one string uses the join() method. Just pass the list of strings as an argument to join(), and it’ll do the magic for you.

Here’s an example:

list_of_strings = ["John", "Charles", "Smith"]
combined_string = " ".join(list_of_strings)
print(combined_string)

Output:

John Charles Smith

You can also change the separator by modifying the string before the join() call. Now let’s say your list has a mix of data types, like integers and strings. No problem! Use the map() function along with join() to handle this situation:

list_of_strings = ["John", 42, "Smith"]
combined_string = " ".join(map(str, list_of_strings))
print(combined_string)

Output:

John 42 Smith

In this case, the map() function converts every element in the list to a string before joining them.

Another solution is using the str.format() method to merge the list elements. This is especially handy when you want to follow a specific template.

For example:

list_of_strings = ["John", "Charles", "Smith"]
result = " {} {} {}".format(*list_of_strings)
print(result)

Output:

John Charles Smith

And that’s it! ๐ŸŽ‰ Now you know multiple ways to convert a list of strings into one string in Python.

Python Convert List of Strings to Comma Separated String

So you’d like to convert a list of strings to a comma-separated string using Python.

Here’s a simple solution that uses the join() function:

string_list = ['apple', 'banana', 'cherry']
comma_separated_string = ','.join(string_list)
print(comma_separated_string)

This code would output:

apple,banana,cherry

Using the join() function is a fantastic and efficient way to concatenate strings in a list, adding your desired delimiter (in this case, a comma) between every element ๐Ÿ˜ƒ.

In case your list doesn’t only contain strings, don’t sweat! You can still convert it to a comma-separated string, even if it includes integers or other types. Just use list comprehension along with the str() function:

mixed_list = ['apple', 42, 'cherry']
comma_separated_string = ','.join(str(item) for item in mixed_list)
print(comma_separated_string)

And your output would look like:

apple,42,cherry

Now you have a versatile method to handle lists containing different types of elements ๐Ÿ˜‰

Remember, if your list includes strings containing commas, you might want to choose a different delimiter or use quotes to better differentiate between items.

For example:

list_with_commas = ['apple,green', 'banana,yellow', 'cherry,red']
comma_separated_string = '"{}"'.format('", "'.join(list_with_commas))
print(comma_separated_string)

Here’s the output you’d get:

"apple,green", "banana,yellow", "cherry,red"

With these tips and examples, you should be able to easily convert a list of strings (or mixed data types) to comma-separated strings in Python ๐Ÿ‘.

Python Convert List Of Strings To Lowercase

Let’s dive into converting a list of strings to lowercase in Python. In this section, you’ll learn three handy methods to achieve this. Don’t worry, they’re easy!

Solution: List Comprehension

Firstly, you can use list comprehension to create a list with all lowercase strings. This is a concise and efficient way to achieve your goal.

Here’s an example:

original_list = ["Hello", "WORLD", "PyThon"]
lowercase_list = [item.lower() for item in original_list]
print(lowercase_list) # Output: ['hello', 'world', 'python']

With this approach, the lower() method is applied to each item in the list, creating a new list with lowercase strings. ๐Ÿš€

Solution: map() Function

Another way to convert a list of strings to lowercase is by using the map() function. This function applies a given function (in our case, str.lower()) to each item in a list.

Here’s an example:

original_list = ["Hello", "WORLD", "PyThon"]
lowercase_list = list(map(str.lower, original_list))
print(lowercase_list) # Output: ['hello', 'world', 'python']

Remember to wrap the map() function with the list() function to get your desired output. ๐Ÿ‘

Solution: For Loop

Lastly, you can use a simple for loop. This approach might be more familiar and readable to some, but it’s typically less efficient than the other methods mentioned.

Here’s an example:

original_list = ["Hello", "WORLD", "PyThon"]
lowercase_list = [] for item in original_list: lowercase_list.append(item.lower()) print(lowercase_list) # Output: ['hello', 'world', 'python']

I have written a complete guide on this on the Finxter blog. Check it out! ๐Ÿ‘‡

๐Ÿ’ก Recommended: Python Convert String List to Lowercase

Python Convert List of Strings to Datetime

In this section, we’ll guide you through converting a list of strings to datetime objects in Python. It’s a common task when working with date-related data, and can be quite easy to achieve with the right tools!

So, let’s say you have a list of strings representing dates, and you want to convert this into a list of datetime objects. First, you’ll need to import the datetime module to access the essential functions. ๐Ÿ—“

from datetime import datetime

Next, you can use the strptime() function from the datetime module to convert each string in your list to a datetime object. To do this, simply iterate over the list of strings and apply the strptime function with the appropriate date format.

For example, if your list contained dates in the "YYYY-MM-DD" format, your code would look like this:

date_strings_list = ["2023-05-01", "2023-05-02", "2023-05-03"]
date_format = "%Y-%m-%d"
datetime_list = [datetime.strptime(date_string, date_format) for date_string in date_strings_list]

By using list comprehension, you’ve efficiently transformed your list of strings into a list of datetime objects! ๐ŸŽ‰

Keep in mind that you’ll need to adjust the date_format variable according to the format of the dates in your list of strings. Here are some common date format codes you might need:

  • %Y: Year with century, as a decimal number (e.g., 2023)
  • %m: Month as a zero-padded decimal number (e.g., 05)
  • %d: Day of the month as a zero-padded decimal number (e.g., 01)
  • %H: Hour (24-hour clock) as a zero-padded decimal number (e.g., 17)
  • %M: Minute as a zero-padded decimal number (e.g., 43)
  • %S: Second as a zero-padded decimal number (e.g., 08)

Python Convert List Of Strings To Bytes

So you want to convert a list of strings to bytes in Python? No worries, I’ve got your back. ๐Ÿ˜Š This brief section will guide you through the process.

First things first, serialize your list of strings as a JSON string, and then convert it to bytes. You can easily do this using Python’s built-in json module.

Here’s a quick example:

import json your_list = ['hello', 'world']
list_str = json.dumps(your_list)
list_bytes = list_str.encode('utf-8')

Now, list_bytes is the byte representation of your original list. ๐ŸŽ‰

But hey, what if you want to get back the original list from those bytes? Simple! Just do the reverse:

reconstructed_list = json.loads(list_bytes.decode('utf-8'))

And voilร ! You’ve successfully converted a list of strings to bytes and back again in Python. ๐Ÿฅณ

Remember that this method works well for lists containing strings. If your list includes other data types, you may need to convert them to strings first.

Python Convert List of Strings to Dictionary

Next, you’ll learn how to convert a list of strings to a dictionary. This can come in handy when you want to extract meaningful data from a list of key-value pairs represented as strings. ๐Ÿ

To get started, let’s say you have a list of strings that look like this:

data_list = ["Name: John", "Age: 30", "City: New York"]

You can convert this list into a dictionary using a simple loop and the split() method.

Here’s the recipe:

data_dict = {} for item in data_list: key, value = item.split(": ") data_dict[key] = value print(data_dict) # Output: {"Name": "John", "Age": "30", "City": "New York"}

Sweet, you just converted your list to a dictionary! ๐ŸŽ‰ But, what if you want to make it more concise? Python offers an elegant solution with dictionary comprehension.

Check this out:

data_dict = {item.split(": ")[0]: item.split(": ")[1] for item in data_list}
print(data_dict) # Output: {"Name": "John", "Age": "30", "City": "New York"}

With just one line of code, you achieved the same result. High five! ๐Ÿ™Œ

When dealing with more complex lists that contain strings in various formats or nested structures, it’s essential to use additional tools like the json.loads() method or the ast.literal_eval() function. But for simple cases like the example above, the loop and dictionary comprehension should be more than enough.

Python Convert List Of Strings To Bytes-Like Object

๐Ÿ’ก How to convert a list of strings into a bytes-like object in Python? It’s quite simple and can be done easily using the json library and the utf-8 encoding.

Firstly, let’s tackle encoding your list of strings as a JSON string ๐Ÿ“. You can use the json.dumps() function to achieve this.

Here’s an example:

import json your_list = ['hello', 'world']
json_string = json.dumps(your_list)

Now that you have the JSON string, you can convert it to a bytes-like object using the encode() method of the string ๐Ÿ”„.

Simply specify the encoding you’d like to use, which in this case is 'utf-8':

bytes_object = json_string.encode('utf-8')

And that’s it! Your list of strings has been successfully transformed into a bytes-like object. ๐Ÿ’ฅ To recap, here’s the complete code snippet:

import json your_list = ['hello', 'world']
json_string = json.dumps(your_list)
bytes_object = json_string.encode('utf-8')

If you ever need to decode the bytes-like object back into a list of strings, just use the decode() method followed by the json.loads() function like so:

decoded_string = bytes_object.decode('utf-8')
original_list = json.loads(decoded_string)

Python Convert List Of Strings To Array

Converting a list of strings to an array in Python is a piece of cake ๐Ÿฐ.

One simple approach is using the NumPy library, which offers powerful tools for working with arrays. To start, make sure you have NumPy installed. Afterward, you can create an array using the numpy.array() function.

Like so:

import numpy as np string_list = ['apple', 'banana', 'cherry']
string_array = np.array(string_list)

Now your list is enjoying its new life as an array! ๐ŸŽ‰

But sometimes, you may need to convert a list of strings into a specific data structure, like a NumPy character array. For this purpose, numpy.char.array() comes to the rescue:

char_array = np.char.array(string_list)

Now you have a character array! Easy as pie, right? ๐Ÿฅง

If you want to explore more options, check out the built-in split() method that lets you convert a string into a list, and subsequently into an array. This method is especially handy when you need to split a string based on a separator or a regular expression.

Python Convert List Of Strings To JSON

You’ve probably encountered a situation where you need to convert a list of strings to JSON format in Python. Don’t worry! We’ve got you covered. In this section, we’ll discuss a simple and efficient method to convert a list of strings to JSON using the json module in Python.

First things first, let’s import the necessary module:

import json

Now that you’ve imported the json module, you can use the json.dumps() function to convert your list of strings to a JSON string.

Here’s an example:

string_list = ["apple", "banana", "cherry"]
json_string = json.dumps(string_list)
print(json_string)

This will output the following JSON string:

["apple", "banana", "cherry"]

๐ŸŽ‰ Great job! You’ve successfully converted a list of strings to JSON. But what if your list contains strings that are already in JSON format?

In this case, you can use the json.loads() function:

string_list = ['{"name": "apple", "color": "red"}', '{"name": "banana", "color": "yellow"}']
json_list = [json.loads(string) for string in string_list]
print(json_list)

The output will be:

[{"name": "apple", "color": "red"}, {"name": "banana", "color": "yellow"}]

And that’s it! ๐Ÿฅณ Now you know how to convert a list of strings to JSON in Python, whether it’s a simple list of strings or a list of strings already in JSON format.

Python Convert List Of Strings To Numpy Array

Are you looking to convert a list of strings to a numpy array in Python? Next, we will briefly discuss how to achieve this using NumPy.

First things first, you need to import numpy. If you don’t have it installed, simply run pip install numpy in your terminal or command prompt.

Once you’ve done that, you can import numpy in your Python script as follows:

import numpy as np

Now that numpy is imported, let’s say you have a list of strings with numbers that you want to convert to a numpy array, like this:

A = ['33.33', '33.33', '33.33', '33.37']

To convert this list of strings into a NumPy array, you can use a simple list comprehension to first convert the strings to floats and then use the numpy array() function to create the numpy array:

floats = [float(e) for e in A]
array_A = np.array(floats)

๐ŸŽ‰ Congratulations! You’ve successfully converted your list of strings to a numpy array! Now that you have your numpy array, you can perform various operations on it. Some common operations include:

  • Finding the mean, min, and max:
mean, min, max = np.mean(array_A), np.min(array_A), np.max(array_A)
  • Reshaping the array:
reshaped_array = array_A.reshape(2, 2)
array_B = np.array([1.0, 2.0, 3.0, 4.0])
result = array_A + array_B

Now you know how to convert a list of strings to a numpy array and perform various operations on it.

Python Convert List of Strings to Numbers

To convert a list of strings to numbers in Python, Python’s map function can be your best friend. It applies a given function to each item in an iterable. To convert a list of strings into a list of numbers, you can use map with either the int or float function.

Here’s an example: ๐Ÿ˜Š

string_list = ["1", "2", "3", "4", "5"]
numbers_int = list(map(int, string_list))
numbers_float = list(map(float, string_list))

Alternatively, using list comprehension is another great approach. Just loop through your list of strings and convert each element accordingly.โœจ

Here’s what it looks like:

numbers_int = [int(x) for x in string_list]
numbers_float = [float(x) for x in string_list]

Maybe you’re working with a list that contains a mix of strings representing integers and floats. In that case, you can implement a conditional list comprehension like this: ๐Ÿค“

mixed_list = ["1", "2.5", "3", "4.2", "5"]
numbers_mixed = [int(x) if "." not in x else float(x) for x in mixed_list]

And that’s it! Now you know how to convert a list of strings to a list of numbers using Python, using different techniques like the map function and list comprehension.

Python Convert List Of Strings To Array Of Floats

๐ŸŒŸ Starting out, you might have a list of strings containing numbers, like ['1.2', '3.4', '5.6'], and you want to convert these strings to an array of floats in Python.

Here’s how you can achieve this seamlessly:

Using List Comprehension

List comprehension is a concise way to create lists in Python. To convert the list of strings to a list of floats, you can use the following code:

list_of_strings = ['1.2', '3.4', '5.6']
list_of_floats = [float(x) for x in list_of_strings]

โœจThis will give you a new list list_of_floats containing [1.2, 3.4, 5.6].

Using numpy. ๐Ÿงฎ

If you have numpy installed or are working with larger arrays, you might want to convert the list of strings to a numpy array of floats.

Here’s how you can do that:

import numpy as np list_of_strings = ['1.2', '3.4', '5.6']
numpy_array = np.array(list_of_strings, dtype=float)

Now you have a numpy array of floats: array([1.2, 3.4, 5.6]). ๐ŸŽ‰

Converting Nested Lists

If you’re working with a nested list of strings representing numbers, like:

nested_list_of_strings = [['1.2', '3.4'], ['5.6', '7.8']]

You can use the following list comprehension:

nested_list_of_floats = [[float(x) for x in inner] for inner in nested_list_of_strings]

This will result in a nested list of floats like [[1.2, 3.4], [5.6, 7.8]]. ๐Ÿš€


Pheww! Hope this article helped you solve your conversion problems. ๐Ÿ˜…

Free Cheat Sheets! ๐Ÿ‘‡

If you want to keep learning Python and improving your skills, feel free to check out our Python cheat sheets (100% free): ๐Ÿ‘‡

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Pandas Series Object โ€“ A Helpful Guide with Examples

5/5 – (1 vote)

If you’re working with data in Python, you might have come across the pandas library. ๐Ÿผ

One of the key components of pandas is the Series object, which is a one-dimensional, labeled array capable of holding data of any type, such as integers, strings, floats, and even Python objects ๐Ÿ˜ƒ.

The Series object serves as a foundation for organizing and manipulating data within the pandas library.

This article will teach you more about this crucial data structure and how it can benefit your data analysis workflows. Let’s get started! ๐Ÿ‘‡

Creating a Pandas Series

In this section, you’ll learn how to create a Pandas Series, a powerful one-dimensional labeled array capable of holding any data type.

To create a Series, you can use the Series() constructor from the Pandas library.

Make sure you have Pandas installed and imported:

import pandas as pd

Now, you can create a Series using the pd.Series() function, and pass in various data structures like lists, dictionaries, or even scalar values. For example:

my_list = [1, 2, 3, 4]
my_series = pd.Series(my_list)

The Series() constructor accepts various parameters that help you customize the resulting series, including:

  • data: This is the input dataโ€”arrays, dicts, or scalars.
  • index: You can provide a custom index for your series to label the values. If you don’t supply one, Pandas will automatically create an integer index (0, 1, 2…).

Here’s an example of creating a Series with a custom index:

custom_index = ['a', 'b', 'c', 'd']
my_series = pd.Series(my_list, index=custom_index)

When you create a Series object with a dictionary, Pandas automatically takes the keys as the index and the values as the series data:

my_dict = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
my_series = pd.Series(my_dict)

๐Ÿ’ก Remember: Your Series can hold various data types, including strings, numbers, and even objects.

Pandas Series Indexing

Next, you’ll learn the best ways to index and select data from a Pandas Series, making your data analysis tasks more manageable and enjoyable.

Again, a Pandas Series is a one-dimensional labeled array, and it can hold various data types like integers, floats, and strings. The series object contains an index, which serves multiple purposes, such as metadata identification, automatic and explicit data alignment, and intuitive data retrieval and modification ๐Ÿ› .

There are two types of indexing available in a Pandas Series:

  1. Position-based indexing – this uses integer positions to access data. The pandas function iloc[] comes in handy for this purpose.
  2. Label-based indexing – this uses index labels for data access. The pandas function loc[] works great for this type of indexing.
YouTube Video

๐Ÿ’ก Recommended: Pandas loc() and iloc() โ€“ A Simple Guide with Video

Let’s examine some examples of indexing and selection in a Pandas Series:

import pandas as pd # Sample Pandas Series
data = pd.Series([10, 20, 30, 40, 50], index=['a', 'b', 'c', 'd', 'e']) # Position-based indexing (using iloc)
position_index = data.iloc[2] # Retrieves the value at position 2 (output: 30) # Label-based indexing (using loc)
label_index = data.loc['b'] # Retrieves the value with the label 'b' (output: 20)

Keep in mind that while working with Pandas Series, the index labels do not have to be unique but must be hashable types. This means they should be of immutable data types like strings, numbers, or tuples ๐ŸŒŸ.

๐Ÿ’ก Recommended: Mutable vs. Immutable Objects in Python

Accessing Values in a Pandas Series

So you’re working with Pandas Series and want to access their values. I already showed you this in the previous section but let’s repeat this once again. Repetition. Repetition. Repetition!

First of all, create your Pandas Series:

import pandas as pd data = ['A', 'B', 'C', 'D', 'E']
my_series = pd.Series(data)

Now that you have your Series, let’s talk about accessing its values ๐Ÿš€:

  1. Using index: You can access an element in a Series using its index, just like you do with lists:
third_value = my_series[2]
print(third_value) # Output: C
  1. Using .loc[]: Access an element using its index label with the .loc[] accessor, which is useful when you have custom index names๐Ÿ”–:
data = ['A', 'B', 'C', 'D', 'E']
index_labels = ['one', 'two', 'three', 'four', 'five']
my_series = pd.Series(data, index=index_labels) second_value = my_series.loc['two']
print(second_value) # Output: B
  1. Using .iloc[]: Access a value based on its integer position with the .iloc[] accessor. This is particularly helpful when you have non-integer index labels๐ŸŽฏ:
value_at_position_3 = my_series.iloc[2]
print(value_at_position_3) # Output: C

Iterating through a Pandas Series

๐Ÿ’ก Although iterating over a Series is possible, it’s generally discouraged in the Pandas community due to its suboptimal performance. Instead, try using vectorization or other optimized methods, such as apply, transform, or agg.

This section will discuss Series iteration methods, but always remember to consider potential alternatives first!

When you absolutely need to iterate through a Series, you can use the iteritems() function, which returns an iterator of index-value pairs. Here’s an example:

for idx, val in your_series.iteritems(): # Do something with idx and val

Another method to iterate over a Pandas Series is by converting it into a list using the tolist() function, like this:

for val in your_series.tolist(): # Do something with val

๐Ÿš€ However, keep in mind that these approaches are suboptimal and should be avoided whenever possible. Instead, try one of the following efficient techniques:

  • Vectorized operations: Apply arithmetic or comparison operations directly on the Series.
  • Use apply(): Apply a custom function element-wise.
  • Use agg(): Aggregate multiple operations to be applied.
  • Use transform(): Apply a function and return a similarly-sized Series.

Sorting a Pandas Series ๐Ÿ”„

Sorting a Pandas Series is pretty straightforward. With the sort_values() function, you can easily reorder your series, either in ascending or descending order.

First, you must import the Pandas library and create a Pandas Series:

import pandas as pd
s = pd.Series([100, 200, 54.67, 300.12, 400])

To sort the values in the series, just use the sort_values() function like this:

sorted_series = s.sort_values()

By default, the values will be sorted in ascending order. If you want to sort them in descending order, just set the ascending parameter to False:

sorted_series = s.sort_values(ascending=False)

You can also control the sorting method using the kind parameter. Supported options are 'quicksort', 'mergesort', and 'heapsort'. For example:

sorted_series = s.sort_values(kind='mergesort')

When dealing with missing values (NaN) in your series, you can use the na_position parameter to specify their position in the sorted series. The default value is 'last', which places missing values at the end.

To put them at the beginning of the sorted series, just set the na_position parameter to 'first':

sorted_series = s.sort_values(na_position='first')

Applying Functions to a Pandas Series

You might come across situations where you want to apply a custom function to your Pandas Series. Let’s dive into how you can do that using the apply() method. ๐Ÿš€

YouTube Video

To begin with, the apply() method is quite flexible and allows you to apply a wide range of functions on your Series. These functions could be NumPy’s universal functions (ufuncs), built-in Python functions, or user-defined functions. Regardless of the type, apply() will work like magic.๐ŸŽฉโœจ

For instance, let’s say you have a Pandas Series containing square numbers, and you want to find the square root of these numbers:

import pandas as pd square_numbers = pd.Series([4, 9, 16, 25, 36])

Now, you can use the apply() method along with the built-in Python function sqrt() to calculate the square root:

import math square_roots = square_numbers.apply(math.sqrt)
print(square_roots)

You’ll get the following output:

0 2.0
1 3.0
2 4.0
3 5.0
4 6.0
dtype: float64

Great job! ๐ŸŽ‰ Now, let’s consider you want to create your own function to check if the numbers in a Series are even. Here’s how you can achieve that:

def is_even(number): return number % 2 == 0 even_numbers = square_numbers.apply(is_even)
print(even_numbers)

And the output would look like this:

0 True
1 False
2 True
3 False
4 True
dtype: bool

Congratulations! ๐Ÿฅณ You’ve successfully used the apply() method with a custom function.

Replacing Values in a Pandas Series

You might want to replace specific values within a Pandas Series to clean up your data or transform it into a more meaningful format. The replace() function is here to help you do that! ๐Ÿ˜ƒ

How to use replace()

To use the replace() function, simply call it on your Series object like this: your_series.replace(to_replace, value). to_replace is the value you want to replace, and value is the new value you want to insert instead. You can also use regex for more advanced replacements.

Let’s see an example:

import pandas as pd data = pd.Series([1, 2, 3, 4])
data = data.replace(2, "Two")
print(data)

This code will replace the value 2 with the string "Two" in your Series. ๐Ÿ”„

Multiple replacements

You can replace multiple values simultaneously by passing a dictionary or two lists to the function. For example:

data = pd.Series([1, 2, 3, 4])
data = data.replace({1: 'One', 4: 'Four'})
print(data)

In this case, 1 will be replaced with 'One' and 4 with 'Four'. ๐ŸŽ‰

Limiting replacements

You can limit the number of replacements by providing the limit parameter. For example, if you set limit=1, only the first occurrence of the value will be replaced.

data = pd.Series([2, 2, 2, 2])
data = data.replace(2, "Two", limit=1)
print(data)

This code will replace only the first occurrence of 2 with "Two" in the Series. โœจ

Appending and Concatenating Pandas Series

You might want to combine your pandas Series while working with your data. Worry not! ๐Ÿ˜ƒ Pandas provides easy and convenient ways to append and concatenate your Series.

Appending Series

Appending Series can be done using the append() method. It allows you to concatenate two or more Series objects. To use it, simply call the method on one series and pass the other series as the argument.

For example:

import pandas as pd series1 = pd.Series([1, 2, 3])
series2 = pd.Series([4, 5, 6]) result = series1.append(series2)
print(result)

Output:

0 1
1 2
2 3
0 4
1 5
2 6
dtype: int64

However, appending Series iteratively may become computationally expensive. In such cases, consider using concat() instead. ๐Ÿ‘‡

Concatenating Series

The concat() function is more efficient when you need to combine multiple Series vertically. Simply provide a list of Series you want to concatenate as its argument, like so:

import pandas as pd series_list = [ pd.Series(range(1, 6), index=list('abcde')), pd.Series(range(1, 6), index=list('fghij')), pd.Series(range(1, 6), index=list('klmno'))
] combined_series = pd.concat(series_list)
print(combined_series)

Output:

a 1
b 2
c 3
d 4
e 5
f 1
g 2
h 3
i 4
j 5
k 1
l 2
m 3
n 4
o 5
dtype: int64

๐Ÿš€ There you have it! You’ve combined your Pandas Series using append() and concat().

Renaming a Pandas Series

Renaming a Pandas Series is a simple yet useful operation you may need in your data analysis process.

To start, the rename() method in Pandas can be used to alter the index labels or name of a given Series object. But, if you just want to change the name of the Series, you can set the name attribute directly. For instance, if you have a Series object called my_series, you can rename it to "New_Name" like this:

my_series.name = "New_Name"

Now, let’s say you want to rename the index labels of your Series. You can do this using the rename() method. Here’s an example:

renamed_series = my_series.rename(index={"old_label1": "new_label1", "old_label2": "new_label2"})

The rename() method also accepts functions for more complex transformations. For example, if you want to capitalize all index labels, you can do it like this:

capitalized_series = my_series.rename(index=lambda x: x.capitalize())

Keep in mind that the rename() method creates a new Series by default and doesn’t modify the original one. If you want to change the original Series in-place, just set the inplace argument to True:

my_series.rename(index={"old_label1": "new_label1", "old_label2": "new_label2"}, inplace=True)

Unique Values in a Pandas Series

To find unique values in a Pandas Series, you can use the unique() method๐Ÿ”. This method returns the unique values in the series without sorting them, maintaining the order of appearance.

Here’s a quick example:

import pandas as pd data = {'A': [1, 2, 1, 4, 5, 4]}
series = pd.Series(data['A']) unique_values = series.unique()
print(unique_values)

The output will be: [1, 2, 4, 5]

When working with missing values, keep in mind that the unique() method includes NaN values if they exist in the series. This behavior ensures you are aware of missing data in your dataset ๐Ÿ“š.

If you need to find unique values in multiple columns, the unique() method might not be the best choice, as it only works with Series objects, not DataFrames. Instead, use the .drop_duplicates() method to get unique combinations of multiple columns.

๐Ÿ’ก Recommended: The Ultimate Guide to Data Cleaning in Python and Pandas

To summarize, when finding unique values in a Pandas Series:

  • Use the unique() method for a single column ๐Ÿงช
  • Remember that NaN values will be included as unique values when present ๐Ÿ“Œ
  • Use the .drop_duplicates() method for multiple columns when needed ๐Ÿ”„

With these tips, you’re ready to efficiently handle unique values in your Pandas data analysis! ๐Ÿผ๐Ÿ’ป

Converting Pandas Series to Different Data Types

You can convert a Pandas Series to different data types to modify your data and simplify your work. In this section, you’ll learn how to transform a Series into a DataFrame, List, Dictionary, Array, String, and Numpy Array. Let’s dive in! ๐Ÿš€

Series to DataFrame

To convert a Series to a DataFrame, use the to_frame() method. Here’s how:

import pandas as pd data = pd.Series([1, 2, 3, 4])
df = data.to_frame()
print(df)

This code will output:

 0
0 1
1 2
2 3
3 4

Series to List

For transforming a Series to a List, simply call the tolist() method, like this:

data_list = data.tolist()
print(data_list)

Output:

[1, 2, 3, 4]

Series to Dictionary

To convert your Series into a Dictionary, use the to_dict() method:

data_dict = data.to_dict()
print(data_dict)

This results in:

{0: 1, 1: 2, 2: 3, 3: 4}

The keys are now indexes, and the values are the original Series data.

Series to Array

Convert your Series to an Array by accessing its .array attribute:

data_array = data.array
print(data_array)

Output:

<PandasArray>
[1, 2, 3, 4]

Series to String

To join all elements of a Series into a single String, use the join() function from the str library:

data_str = ''.join(map(str, data))
print(data_str)

This will result in:

1234

Series to Numpy Array

For converting a Series into a Numpy Array, call the to_numpy() method:

import numpy as np data_numpy = data.to_numpy()
print(data_numpy)

Output:

array([1, 2, 3, 4], dtype=int64)

Now you’re all set to manipulate your Pandas Series objects and adapt them to different data types! ๐ŸŽ‰

Python Pandas Series in Practice ๐Ÿผ๐Ÿ’ป

A Pandas Series is a one-dimensional array-like object that’s capable of holding any data type. It’s one of the essential data structures in the Pandas library, along with the DataFrame. Series is an easy way to organize and manipulate your data, especially when dealing with labeled data, such as SQL databases or dictionary keys. ๐Ÿ”‘โšก

To begin, import the Pandas library, which is usually done with the alias ‘pd‘:

import pandas as pd

Creating a Pandas Series ๐Ÿ“๐Ÿ”จ

To create a Series, simply pass a list, ndarray, or dictionary to the pd.Series() function. For example, you can create a Series with integers:

integer_series = pd.Series([1, 2, 3, 4, 5])

Or with strings:

string_series = pd.Series(['apple', 'banana', 'cherry'])

In case you want your Series to have an explicit index, you can specify the index parameter:

indexed_series = pd.Series(['apple', 'banana', 'cherry'], index=['a', 'b', 'c'])

Accessing and Manipulating Series Data ๐Ÿšช๐Ÿ”ง

Now that you have your Series, here’s how you can access and manipulate the data:

  • Accessing data by index (using both implicit and explicit index):
    • First item: integer_series[0] or indexed_series['a']
    • Slicing: integer_series[1:3]
  • Adding new data:
    • Append: string_series.append(pd.Series(['date']))
    • Add with a label: indexed_series['d'] = 'date'
  • Common Series methods:
    • all() – Check if all elements are true
    • any() – Check if any elements are true
    • unique() – Get unique values
    • ge(another_series) – Compare elements element-wise with another Series

These are just a few examples of interacting with a Pandas Series. There are many other functionalities you can explore!

Practice makes perfect, so feel free to join our free email academy where I’ll show you practical coding projects, data science, exponential technologies in AI and blockchain engineering, Python, and much more. How can you join? Simply download your free cheat sheets by entering your name here:

Let your creativity run wild and happy coding! ๐Ÿค—๐Ÿ’ก

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pvlib Python: A Comprehensive Guide to Solar Energy Simulation

5/5 – (1 vote)

If you’re interested in simulating the performance of photovoltaic energy systems, pvlib Python is a tool that can provide you with a set of functions and classes to do just that ๐ŸŒž. Developed as a community-supported project, it was originally ported from the PVLIB MATLAB toolbox created at Sandia National Laboratories, incorporating numerous models and methodologies from the Labs ๐Ÿงช.

As you dive into pvlib Python, you’ll discover its powerful capabilities in modeling photovoltaic systems. By leveraging the extensive package, you can accurately simulate system performance and plan the best possible setup for your solar projects โšก.

Keep in mind that being an open-source project, it constantly evolves thanks to the combined efforts of developers from all around the world ๐ŸŒ.

In your journey with pvlib Python, you’ll be able to optimize energy production from photovoltaic installations and contribute to the shared knowledge and improvement of solar power solutions ๐ŸŒฑ.

Overview of PVLIB Python

History and Background

PVLIB Python ๐Ÿ is a powerful tool that was originally ported from the PVLIB MATLAB toolbox. Developed at Sandia National Laboratories, it now provides you with functions and classes for simulating the performance of photovoltaic energy systems โ˜€.

Key Components

With PVLIB Python, you can:

  • Retrieve irradiance and weather data ๐ŸŒฆ
  • Calculate solar position โ˜€
  • Model photovoltaic (PV) system components ๐Ÿ”ง

You will find it versatile, as it implements many models and methods from the PVPMC modeling diagram. To make your job even easier, PVLIB Python’s documentation has theory topics, an intro tutorial, an example gallery, and an API reference ๐Ÿ“š.

Community Supported Tool

PVLIB Python is a community-supported tool available on GitHub, which means you are encouraged to collaborate with fellow users, contribute to its growth, and stay up to date with the latest versions. By being a part of this community, you’ll be among those who benefit from new features, bug fixes, and performance improvements ๐ŸŒ.

To sum it up, PVLIB Python equips you with the necessary tools to model and simulate photovoltaic energy systems, enriching your understanding of PV performance ๐Ÿ‘ฉโ€๐Ÿ’ผ๐Ÿ‘จโ€๐Ÿ’ผ.

Installing PVLIB Python ๐Ÿš€

Before diving headfirst into using PVLIB Python, you need to install it on your system. Don’t worry; it’s a breeze! Just follow these simple steps. Keep in mind that PVLIB Python requires the following packages: numpy and pandas.

To install PVLIB Python, use pip by running the command in your terminal:

pip install pvlib

๐ŸŽ‰ Congrats! You’ve successfully installed PVLIB Python.

If you want to experiment with the NREL SPA algorithm, follow these instructions:

  1. Obtain the source code by downloading the pvlib repository.
  2. Download the SPA files from NREL.
  3. Copy the SPA files into pvlib-python/pvlib/spa_c_files.
  4. From the pvlib-python directory, run:
pip uninstall pvlib
pip install .

That’s all it takes! You’re all set for exploring PVLIB Python and simulating photovoltaic energy systems performance. Happy coding! ๐Ÿ’ป๐ŸŒž

PVLIB Python Models and Methods

Models

PVLIB Python provides a variety of models for simulating the performance of photovoltaic energy systems ๐ŸŒž. Originally ported from the PVLIB MATLAB toolbox developed at Sandia National Laboratories, it implements many of the models and methods used in PV performance modeling programs.

You’ll find models for irradiance and clear sky data, solar position, atmospheric and temperature data, as well as modules and inverter specifications. Utilizing these models, you can accurately predict the performance of your PV system based on various factors ๐Ÿ“Š.

Methods

Beyond the models, PVLIB Python also implements various methods to streamline the calculation and analytical processes associated with PV energy systems ๐Ÿ’ก.

These methods help determine system output by computing factors like irradiance components, spectral loss, and temperature coefficients. PVLIB provides methods for various tracking algorithms and translation functions that transform diffuse irradiance to the plane of array.

Additionally, PVLIB Python offers a collection of classes that cater to users with a preference for object-oriented programming ๐Ÿ–ฅ.

Functions

In its documentation, PVLIB Python offers a comprehensive set of functions and classes for various tasks essential in simulating the performance of a PV energy system. Some essential functions include:

  • Functions for calculating solar position and extraterrestrial radiation ๐Ÿ’ซ
  • Functions for clear sky irradiance and atmospheric transmittance โ˜
  • Functions for processing irradiance data and PV module data โšก
  • Functions for modeling PV system components like DC and AC power output ๐Ÿ”‹

By combining and implementing these functions, you can create a detailed and accurate simulation of your PV system under varying conditions and parameters ๐ŸŒ.

PVLIB Example

The following code example calculates the annual energy yield of photovoltaic systems at different locations using the PVLIB library. It creates a function calculate_annual_energy() that takes in location coordinates, TMY3 weather data, module parameters, temperature model parameters, and inverter parameters.

The function uses PVLIB’s ModelChain to simulate the energy yield for each location and stores the results in a pandas Series. Finally, the code prints and plots the annual energy yield in a bar chart for visual comparison.

import pandas as pd
import matplotlib.pyplot as plt
from pvlib.pvsystem import PVSystem, Array, FixedMount
from pvlib.location import Location
from pvlib.modelchain import ModelChain def calculate_annual_energy(coordinates, tmys, module, temperature_model_parameters, inverter): energies = {} for location, weather in zip(coordinates, tmys): latitude, longitude, name, altitude, timezone = location loc = Location(latitude, longitude, name=name, altitude=altitude, tz=timezone) mount = FixedMount(surface_tilt=latitude, surface_azimuth=180) array = Array( mount=mount, module_parameters=module, temperature_model_parameters=temperature_model_parameters, ) system = PVSystem(arrays=[array], inverter_parameters=inverter) mc = ModelChain(system, loc) mc.run_model(weather) annual_energy = mc.results.ac.sum() energies[name] = annual_energy return pd.Series(energies) energies = calculate_annual_energy(coordinates, tmys, module, temperature_model_parameters, inverter)
print(energies) energies.plot(kind='bar', rot=0)
plt.ylabel('Yearly energy yield (W hr)')
plt.show()

This code snippet defines a function calculate_annual_energy() that computes the annual energy yield for different locations using the PVLIB library. It then prints the energies and plots them in a bar chart.

Here’s a detailed explanation of the code:

  1. Import necessary libraries:
    • pandas for handling data manipulation and analysis
    • matplotlib.pyplot for creating plots and visualizations
    • PVSystem, Array, and FixedMount from pvlib.pvsystem for modeling photovoltaic systems
    • Location from pvlib.location for creating location objects
    • ModelChain from pvlib.modelchain for simulating the energy yield of a photovoltaic system
  2. Define the calculate_annual_energy() function:
    • The function takes five arguments:
      • coordinates: a list of tuples containing location information (latitude, longitude, name, altitude, and timezone)
      • tmys: a list of TMY3 weather data for each location in the coordinates list
      • module: a dictionary containing photovoltaic module parameters
      • temperature_model_parameters: a dictionary containing temperature model parameters
      • inverter: a dictionary containing inverter parameters
  3. Initialize an empty dictionary energies to store the annual energy yield for each location.
  4. Loop through the coordinates and tmys lists simultaneously using the zip() function:
    • Extract the latitude, longitude, name, altitude, and timezone from the location tuple
    • Create a Location object loc with the extracted information
    • Create a FixedMount object mount with the surface tilt equal to the latitude and surface azimuth equal to 180 (facing south)
    • Create an Array object array with the mount, module_parameters, and temperature_model_parameters
    • Create a PVSystem object system with the arrays and inverter_parameters
    • Create a ModelChain object mc with the system and loc
    • Run the model with the TMY3 weather data weather
    • Calculate the annual energy by summing the AC output (mc.results.ac.sum()) and store it in the energies dictionary with the location name as the key
  5. Return a pandas Series object created from the energies dictionary.
  6. Call the calculate_annual_energy() function with the required input variables (coordinates, tmys, module, temperature_model_parameters, and inverter), and store the result in the energies variable.
  7. Print the energies pandas Series.
  8. Create a bar plot of the energies pandas Series, rotating the x-axis labels to 0 degrees and setting the y-axis label to 'Yearly energy yield (W hr)'. Finally, display the plot using plt.show().

PVLIB Matlab Toolbox

As someone interested in simulating the performance of photovoltaic energy systems, you’ll appreciate the PVLIB Matlab Toolbox. This is a set of well-documented functions designed to model PV system performance ๐ŸŒž, and it was developed at Sandia National Laboratories (SNL). The toolbox has evolved into the PVLIB Python version we know today, but the Matlab version is still available and useful for those who prefer it or are working within a Matlab environment.

Now, let’s dive into some of the features you’ll find in the PVLIB Matlab Toolbox! It consists of various functions tailored to achieve tasks such as solar position calculations, irradiance and temperature models, and direct current power modeling. As a user of this toolbox, you can compare various PV systems and assess their performance โ˜€.

One thing you’ll love as a user of PVLIB Matlab Toolbox is the active community support ๐ŸŽ‰. The development of the toolbox, as well as its Python counterpart, is rooted in the collaboration of the PV Performance Modeling Collaborative (PVPMC). So, if you encounter any challenges or require assistance, there is a community of experts ready to help and contribute to the ongoing development of the toolbox.

In terms of accessibility, the PVLIB Matlab Toolbox is also available in a Python version, called PVLIB Python. If you are more comfortable working in Python or your projects are in this programming language, PVLIB Python retains the models and methods that made the Matlab Toolbox valuable while also building upon its core capabilities with new features and enhancements ๐Ÿš€.

Projects, Tutorials, and Publications

In this section, you’ll learn about various projects and publications that utilize pvlib Python.

Journal Articles

One notable publication using pvlib Python is by William F. Holmgren, Clifford W. Hansen, and Mark A. Mikofski. They authored a paper titled pvlib python: a python package for modeling solar energy systems. This paper is published in the Journal of Open Source Software and focuses on solar energy system modeling using the pvlib python package.

When citing this paper, you can use the DOI provided or find the publication on zenodo.org. Make sure to check the installation page for using pvlib python in your research. ๐Ÿงช

Commercial Projects

In the commercial space, pvlib python has been adopted by various companies as a valuable tool for simulating the performance of photovoltaic energy systems. These organizations include scientific laboratories, private industries, and other sectors that require accurate solar energy system modeling. ๐Ÿญ

Publicly-Available Applications

A number of publicly-available applications also take advantage of pvlib python. A GitHub wiki page lists various projects and publications using this comprehensive package for modeling solar energy systems, offering inspiration and a potential listing for your application.

As you work with pvlib python, remember to adhere to the variable naming convention to ensure consistency throughout the library. This will help you and others collaboratively build more robust solar energy system models. โ˜€

Wiki and Documentation

Discover how to get started with pvlib Python through its official documentation. This comprehensive guide will help you explore pvlib Python’s functions and classes for simulating the performance of photovoltaic energy systems. Make the most of your pvlib Python experience by referring to the community-supported online wiki containing tutorials and sample projects for newcomers.

Check out this and more graphics at the official source: https://pvsc-python-tutorials.github.io/PVSC48-Python-Tutorial/Tutorial%200%20-%20Overview.html

Jupyter Notebook Tutorials

๐Ÿ““ Enhance your learning with Jupyter Notebook tutorials designed to offer hands-on experience in simulating PV systems. Through interactive examples, you’ll go from understanding common PV systems data to modeling the energy output of a single-axis tracker system. Access these tutorials here.

Solar Power Forecasting Tool

You might be interested in the solar power forecasting tool provided by pvlib Python. This community-supported tool offers a set of functions and classes for simulating the performance of photovoltaic energy systems. Pvlib Python was initially a port of the PVLIB MATLAB toolbox developed at Sandia National Laboratories ๐ŸŒž (source)

J. S. Stein, R.W. Andrews, A.T. Lorenzo, J. Forbess, and D.G. Groenendyk are among the experts who contributed to the development of an open-source solar power forecasting tool using the pvlib Python library. This tool aims to efficiently model and analyze photovoltaic systems, offering features that enable users like you to better understand solar power forecasting ๐Ÿ“Š (source)

What makes pvlib Python a powerful resource for you is its well-documented functions for simulating photovoltaic system performance. It can help you forecast solar power production based on various parameters, enabling you to make informed decisions on your solar energy projects ๐ŸŒ (source)

When using pvlib Python, you’ll appreciate the flexibility of choosing from different models and methods for both weather forecast data and solar power prediction, addressing your specific needs or research interests โ˜€ (source)

So, if solar power forecasting is essential for you, give pvlib Python a try and explore the possibilities it offers. Remember, pvlib Python is part of the growing open-source community, and it’s continuously evolving, ensuring that it stays on top of the latest advancements in photovoltaic energy systems ๐Ÿ”‹(source)


Thanks for reading the whole tutorial! โ™ฅ If you want to stay up-to-date with the latest developments in Python and check out our free Python cheat sheets, feel free to download all of them here:

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Python Container Types: A Quick Guide

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If you’re working with Python, most of the data structures you’ll think about are container types. ๐Ÿš‚๐Ÿšƒ๐Ÿšƒ๐Ÿšƒ

These containers are special data structures that hold and manage collections of elements. Python’s most commonly used built-in container types include tuples, lists, dictionaries, sets, and frozensets ๐Ÿ“ฆ. These containers make it easy for you to store, manage and manipulate your data effectively and efficiently. ๐Ÿ˜ƒ

You might be wondering what makes each container unique. Well, they all serve different purposes and have distinct characteristics.

  • For instance, lists are mutable and ordered, allowing you to add, remove, or modify elements.
  • Tuples, on the other hand, are immutable and ordered, which means once created, their elements cannot be changed โœจ.
  • Dictionaries are mutable and store key-value pairs, making it efficient for data retrieval.
  • Lastly, sets and frozensets are unordered collections, with sets being mutable and frozensets immutable.

As you explore Python, understanding these container types is essential, as they provide a foundation for organizing and manipulating your data.

Basic Built-In Container Types

You might be wondering about Python’s built-in container types. Let’s dive into them and see how useful they can be! ๐Ÿ˜ƒ

List

A List is a mutable sequence type in Python. It allows you to store a collection of objects in a defined order. With lists, you can add, remove or change items easily.

Example of creating a list:

your_list = [1, 2, 3, 4]

Some handy list methods are:

Feel free to dive into our full guide on lists here:

๐Ÿ’ก Recommended: The Ultimate Guide to Python Lists

Tuple

A Tuple is an immutable sequence type. It’s similar to a list but cannot be modified once created. This makes tuples ideal for storing fixed sets of data.

Example of creating a tuple:

your_tuple = (1, 2, 3)

Since it’s immutable, fewer methods are available compared to lists:

  • count(x): Counts the occurrences of x in the tuple
  • index(x): Finds the index of the first occurrence of x

Again, we have created a full guide on tuples here:

๐Ÿ’ก Recommended: The Ultimate Guide to Python Tuples

Set

A set is an unordered collection of unique elements. Sets can help you manage distinct items, and they can be mutable or immutable (frozenset).

Example of creating a set:

your_set = {1, 2, 3, 3}

A few useful set operations include:

๐Ÿ’ก Recommended: The Ultimate Guide to Python Sets

Dict

The dictionary, or dict, is a mutable mapping type. This container allows you to store key-value pairs efficiently.

Example of creating a dict:

your_dict = {'a': 1, 'b': 2, 'c': 3}

Some helpful dict methods are:

  • get(key, default): Gets the value for the key or returns the default value if not found
  • update(iterable): Merges the key-value pairs from iterable into the dictionary
  • pop(key, default): Removes and returns the value for the key or returns the default value if not found

๐Ÿ’ก Recommended: The Ultimate Guide to Python Dictionaries

That’s a quick rundown of Python’s basic built-in container types!

Advanced Container Types from Collections Module

The Python built-in containers, such as list, tuple, and dictionary, can be sufficient for many cases. However, when you need more specialized or high-performance containers, the collections module comes to the rescue ๐Ÿ’ช.

Let’s explore some of these advanced container types:

Namedtuple

Ever struggled with using tuples to store data, leading to unreadable and error-prone code? ๐Ÿค” The namedtuple class is your answer! Namedtuples are similar to regular tuples, but each element has a name for better readability and maintenance ๐Ÿ’ก:

from collections import namedtuple Person = namedtuple("Person", ["name", "age", "city"])
person1 = Person("Alice", 30, "New York")
print(person1.name) # Output: Alice

Now, you can access tuple elements by name instead of index, making your code more readable and less error-prone ๐Ÿ‘Œ.

๐Ÿ’ก Recommended: Python Named Tuple Methods

Deque

If you need a high-performance, double-ended queue, look no further than the deque class. Deques allow you to efficiently append or pop items from both ends of the queue, which can be useful in various applications, such as maintaining a fixed-size history of events ๐Ÿ•ฐ:

from collections import deque dq = deque(maxlen=3)
for i in range(5): dq.append(i) print(dq) # Output:
# deque([0], maxlen=3)
# deque([0, 1], maxlen=3)
# deque([0, 1, 2], maxlen=3)
# deque([1, 2, 3], maxlen=3)
# deque([2, 3, 4], maxlen=3)

With deque, you can keep your data structures efficient and clean ๐Ÿ˜Ž.

ChainMap

Do you have multiple dictionaries that you want to treat as a single unit? The ChainMap class can help! It allows you to link several mappings together, making it easy to search, update or delete items across dictionaries ๐Ÿ“š:

from collections import ChainMap dict1 = {"a": 1, "b": 2}
dict2 = {"b": 3, "c": 4}
chain_map = ChainMap(dict1, dict2)
print(chain_map["b"]) # Output: 2, as it takes the value from the first dictionary

With ChainMap, you can work with multiple dictionaries as if they were one, simplifying your code and making it more efficient ๐Ÿ˜‰.

Counter

Counting items in a collection can be a repetitive task. Luckily, the Counter class can help you keep track of elements and their counts with ease ๐Ÿ’ฏ:

from collections import Counter data = [1, 2, 3, 2, 1, 3, 1, 1]
count = Counter(data)
print(count) # Output: Counter({1: 4, 2: 2, 3: 2})

Now you can easily count items in your collections, making your code more concise and efficient ๐Ÿš€.

OrderedDict

If you need a dictionary that maintains the insertion order of items, the OrderedDict class is perfect for you! Although Python 3.7+ dictionaries maintain order by default, OrderedDict can be useful when working with older versions or when you want to explicitly show that order matters ๐Ÿ“:

from collections import OrderedDict od = OrderedDict()
od["a"] = 1
od["b"] = 2
od["c"] = 3
print(list(od.keys())) # Output: ['a', 'b', 'c']

OrderedDict ensures that your code behaves consistently across Python versions and emphasizes the importance of insertion order ๐ŸŒŸ.

Defaultdict

When working with dictionaries, do you often find yourself initializing default values? The defaultdict class can automate that for you! Just provide a default factory function, and defaultdict will create default values for missing keys on the fly โœจ:

from collections import defaultdict dd = defaultdict(list)
dd["a"].append(1)
dd["b"].append(2)
dd["a"].append(3) print(dd) # Output: defaultdict(, {'a': [1, 3], 'b': [2]})

With defaultdict, you can keep your code free of repetitive default value initializations and make your code more Pythonic ๐Ÿ.


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