Pandas, an open-source data analysis and manipulation library for Python, is a tool of choice for many professionals in data science. Its advanced features and capabilities enable users to manipulate, analyze, and visualize data efficiently.

Recommended: 10 Minutes to Pandas (in 5 Minutes)

In the above video “Making $65 per Hour on Upwork with Pandas” , the highlighted strategy is centered on mastering this versatile tool and effectively communicating its benefits to potential clients. A key fact to remember is that Pandas is highly valued in various industries, including finance, retail, healthcare, and technology, where data is abundant and insights are critical.

For a freelancer, proficiency in Pandas can command an hourly rate of $65 or more, even if it’s just a side business to add an additional and independent income stream.

But it’s not just about the tool; it’s about showcasing your ability to drive business value.

Recommended: Python Freelancer Course – How to Create a Thriving Coding Business Online

Highlighting case studies where you’ve used Pandas to extract meaningful insights or solve complex business problems can significantly boost your profile’s appeal.

As for project bidding, understanding the client‘s requirements and tailoring your proposal to highlight how your Pandas expertise can meet those needs is vital. Negotiation, too, plays a critical role in securing a lucrative rate.

Mastering Pandas and marketing this skill effectively can unlock high-paying opportunities on platforms like Upwork, as demonstrated by the impressive $65 per hour rate (for a freelancer with very little practical experience). This reinforces the importance of specialized skills in enhancing your freelancing career.

Recommended: What’s the Average Python Developer Salary in the US? Six Figures!

This quick guide assumes you have already set up Auto-GPT. If you haven’t, follow our in-depth guide on the Finxter blog.

Use ./run.sh --help (Linux/macOS) or .\run.bat --help (Windows) to list command line arguments. For Docker, substitute docker-compose run --rm auto-gpt in examples.

Common Auto-GPT arguments include: --ai-settings <filename>, --prompt-settings <filename>, and --use-memory <memory-backend>. Short forms such as -m for --use-memory exist. Substitute any angled brackets (<>) with your desired values.

Enable Text-to-Speech using ./run.sh --speak.

Use continuous mode (potentially hazardous, may run indefinitely) with ./run.sh --continuous. Exit with Ctrl+C.

Use Self-Feedback mode (increases token usage, costs) by entering S in the input field.

Run GPT-3.5 only mode with ./run.sh --gpt3only or set SMART_LLM_MODEL in .env to gpt-3.5-turbo. For GPT-4 only, use ./run.sh --gpt4only (raises API costs).

Find logs in ./output/logs, debug with ./run.sh --debug.

Disable commands by setting DISABLED_COMMAND_CATEGORIES in .env. For instance, to disable coding features, use:

DISABLED_COMMAND_CATEGORIES=autogpt.commands.analyze_code,autogpt.commands.execute_code,autogpt.commands.gi.

---

Okay, this was dry, here’s a more fun article:

Recommended: 30 Creative AutoGPT Use Cases to Make Money Online

Open-source LLMs have taken the world by storm in just a little over 2 months, ever since LLaMA’s weights were made available for anyone to tinker and play with. Just less than 2 weeks after the untrained LLaMA model was released by Meta.

A model’s weights are the values set to each parameter after training the model on a dataset, with the parameters being various factors (such as token size, number of layers) that allow the model to give more complex answers to what’s input by the user.

This led to a flurry of advancements from dedicated open-source community members. Through just the use of their personal hardware, they were able to make leaps and bounds in their quest to place the most powerful AI in the hands of everyday people.

Recommended: A Quick and Dirty Dip Into Cutting-Edge Open-Source LLM Research

OpenAI’s leadership seems to have taken quite a notice of these events, because they seem to be planning to release an open-source LLM, according to a report by Reuters. It’s virtually unanimous that OpenAI’s GPT-4 is the best-performing LLM model out there. So an open-source model from them would be no small event, even if it is weaker than GPT-4.

Finding out exactly how an OpenAI foundation model is built would give the open-source community a wealth of knowledge that they can apply to their other projects.

It would also go to show how seriously OpenAI views open source and the community surrounding it. It would show that they’re fully aware that the only chance for them to maintain their LLM dominance is if they allowed the world to improve and iterate on their designs.

Open-source showing such swift and definite progress toward taking the crown away from OpenAI can hardly be a surprise. The law of the wisdom of the crowd was foretelling of that. The insight and understanding of the relative few can never match the capability of the collective knowledge and experience of the tens of millions.

The Ultimate Open-Source LLM Battle – Who Wins?

In a chatbot arena site managed by LYMSYS, visitors are asked to enter a prompt, and two randomly-selected models will each provide a response.

The model that the user chooses as having given the best response is then raised up on the leaderboard while the other gets lowered.

The following models are the top three highest-performing models in that arena, just behind GPT-4 (ELO rating of 1274), Anthropic’s Claude (rating of 1224), and GPT-3.5-Turbo (rating of 1155).

Vicuna-13B

Trained by LYMSYS, an open research organization based in UC Berkeley, it is the most promising model from the LLaMA leak.

Recommended: 11 Best ChatGPT Alternatives

It reportedly achieves 90% response quality compared to ChatGPT and Google’s Bard, using a casual evaluation method done through GPT-4. They were able to accomplish this with just a training cost of $300. It has a rating of 1083.

Koala-13B

Coming from BAIR, another group within UC Berkeley, this is a dialogue model meant for academic research. It aims to answer the question of whether open-source models can overcome the massive scale advantage of closed models through better curation of training data. It comes in with a rating of 1022.

RWKV-4-Raven-14B

Impressively, this model was developed by a single person known by the username BlinkDL.

Even more impressively, it’s an RNN LLM (Recurrent Neural Network) rather than the ubiquitous Transformer LLM. The advent of Transformers is what led to the power of GPT-4 being achieved.

People like BlinkDL figuring out ways to optimize more archaic architectures could soon lead to a hybrid architecture that overtakes Transformers in both performance and speed. This model’s rating is a respectable 989.

Civilization-Defining Power Through Artificial General Intelligence

Open-source is a term that can bring out patronizing feelings in people because, after all, a lot of the best programs we know today are closed-source and are chosen by billions of people each year. But that is only due to there being no real reason for the wider community to develop superior open-source alternatives preferred by the wider public.

It’s a much different case with AI.

A few companies holding such immense and civilization-defining power for themselves is not a future that anyone who truly understands the capabilities of AI would want.

Artificial general intelligence is just around the corner, and with it, a complete reimagining of society as we know it. It is a tool that every single person should have equal access to. That reality would bring about a golden age that humanity has never before experienced in all its history.

No matter what anyone says, hoarding any AI knowledge for oneself is a complete disservice to the good of humanity.

Rather than being reserved for the privileged few, a world where AI can be developed and iterated upon by any and all is the only way any sort of utopia can be achieved. Through open-source AI, the dreams and optimism of some of our favorite sci-fi stories will finally be brought to life.

Recommended: MiniGPT-4: The Latest Breakthrough in Language Generation Technology

Imagine you have a project that requires you to process tons of text files, and these files are scattered throughout your folder hierarchy. By the time you finish reading this article, you’ll be equipped with the knowledge to efficiently fetch all the .txt files in any folder using Python.

Method 1: The Os Module

The os module can be used to interact effectively with the file system. The method os.listdir() lists all files and directories in your target folder. You’ll use this method along with a for loop and the endswith() method to filter .txt files specifically.

Here’s the code snippet:

import os directory = './your_folder/'

txt_files = [] for file in os.listdir(directory): if file.endswith('.txt'): txt_files.append(file) print(txt_files)

This code imports the os module, sets the target directory, and initializes an empty list.

The for loop iterates through all the files and checks for the .txt extension using the endswith() method. Matching files are added to the list, which is printed at the end.

Method 2: The Glob Module (My Fav )

Another solution involves using the glob module, which allows you to find all the file paths in a directory that match a specific pattern. You can use the glob.glob() function to list all .txt files.

Here’s how you can do it:

import glob directory = './your_folder/'

txt_files = glob.glob(f'{directory}*.txt') print(txt_files)

This method imports the glob module, sets the target directory, and retrieves the list of text files using the glob.glob() function that filters file paths based on the given pattern (*.txt). The list of .txt files is then printed.

Method 3: os.listdir() and List Comprehension

The os.listdir() is a simple method to use when listing all files in a directory. You can iterate over all files obtain with this method using a simple list comprehension statement such as [file for file in os.listdir(dir_path) if file.endswith(".txt")].

See this example:

import os dir_path = "your_directory_path"

all_files = os.listdir(dir_path)

txt_files = [file for file in all_files if file.endswith(".txt")] print(txt_files)

This code will list all the text files in the specified directory using os.listdir function.

Method 4: Using os.scandir()

The os.scandir() method can provide more information about each file. Extracting the files from this more information-rich representation is a bit less concise but works just fine in this list comprehension [entry.name for entry in os.scandir(dir_path) if entry.name.endswith(".txt") and entry.is_file()].

For instance, use the following code:

import os dir_path = "your_directory_path"

txt_files = [entry.name for entry in os.scandir(dir_path) if entry.name.endswith(".txt") and entry.is_file()] print(txt_files)

Method 5: Using glob.glob()

For a more concise solution, try the glob.glob() function from the glob library. Here’s the code snippet to list text files:

import glob dir_path = "your_directory_path"

txt_files = glob.glob(f"{dir_path}/*.txt") print(txt_files)

The glob.glob() function returns a list of all text files with the specified pattern (in this case, *.txt).

Method 6: Using pathlib.Path.iterdir()

Finally, the pathlib.Path.iterdir method offers another way to list text files in a directory. To use this method, simply import the pathlib library and write the following code:

from pathlib import Path dir_path = Path("your_directory_path")

txt_files = [file.name for file in dir_path.iterdir() if file.is_file() and file.name.endswith(".txt")] print(txt_files)

In this code, pathlib.Path.iterdir is iterator over the files in the directory and, when combined with list comprehensions, can efficiently list all text files.

Iterating Through Directories

In this section, you’ll learn how to iterate through directories using Python and get all the .txt files in a folder.

We’ll cover three methods: using the for loop method, working with the os.walk() function, and recursively traversing directories with a custom recursive function.

Using the For Loop Method

To get started, we’ll use the os.listdir() function with a for loop. This approach allows you to iterate over all files in a directory and filter by their extension.

This code lists all the .txt files in the specified directory using a simple for loop.

import os directory = 'your_directory_path'

for filename in os.listdir(directory): if filename.endswith('.txt'): print(os.path.join(directory, filename))

Working with the os.walk() Function

The os.walk() function is another powerful tool for iterating over files in directories. It enables you to traverse a directory tree and retrieve all files with a specific extension:

import os root_dir = 'your_directory_path'

for root, dirs, files in os.walk(root_dir): for file in files: if file.endswith('.txt'): print(os.path.join(root, file))

This code explores the entire directory tree, including subdirectories, and prints out the full paths of .txt files.

In fact, we have written a detailed article with a video on the function, feel free to check it out!

Recommended: Python os.walk() – A Simple Illustrated Guide

Recursively Traversing Directories with a Recursive Function

Lastly, you could create a custom recursive function to traverse directories and collect .txt files. This method is particularly useful when working with different operating systems, like Windows and Unix:

from pathlib import Path def find_txt_files(path: Path): txt_files = [] for item in path.iterdir(): if item.is_dir(): txt_files.extend(find_txt_files(item)) elif item.name.endswith('.txt'): txt_files.append(item) return txt_files directory = Path('your_directory_path')

txt_files = find_txt_files(directory)

print(txt_files)

This recursive function explores directories and subdirectories and returns a list of .txt files. This approach is more versatile as it leverages Python 3’s pathlib module.

Filtering Based on File Extension and Size

To get all the .txt files in a folder, you can use the glob module in Python, which provides an easy way to find files matching a specific pattern.

Here’s a simple code snippet to get started:

import glob txt_files = glob.glob('path/to/your/folder/*.txt')

print(txt_files)

This code will provide the absolute paths of all the .txt files within the specified folder.

Now that you have the .txt files, you might want to filter them based on their size. To achieve this, you can use the os module.

Here’s an example of how to filter .txt files by size:

import os

import glob min_size = 1000 # Replace with your desired minimum file size in bytes txt_files = glob.glob('path/to/your/folder/*.txt')

filtered_files = [file for file in txt_files if os.path.getsize(file) >= min_size] print(filtered_files)

In this code, min_size represents the minimum file size in bytes that you wish to retrieve. By using a list comprehension with a condition, you can filter out the files that don’t meet your size requirements.

If you want to find .txt files not only in the target folder but also within its subdirectories, you can use the ** pattern along with the recursive parameter:

txt_files = glob.glob('path/to/your/folder/**/*.txt', recursive=True)

Using this approach, you can easily tailor your search to retrieve specific .txt files based on their size and location. With these tools at hand, you should be able to efficiently filter files in your Python projects.

Operating System Compatibility

Python works well across different operating systems, including Unix and Windows. Thanks to its compatibility , you can consistently use your code on different platforms. For this task, both the os and glob libraries are compatible with Unix and Windows systems, so you don’t have to worry about your text file retrieval code failing on either OS.

To get all the text files in a folder using Python, you can use the os and glob libraries. This works for all operating systems, i.e., Linux, Windows, Ubuntu, macOS.

Here’s a code snippet to achieve this:

import os

import glob os.chdir("your_directory_path")

txt_files = glob.glob('*.txt')

print(txt_files)

Replace “your_directory_path” with the path of your folder containing the txt files.

Recommended Video