Pandas
NumPy
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
WEKA
Huginn
n8n.io
ifttt
Zapier
Microsoft Power Automate
Make.com
Node-RED
Skyvia
Pandas HuginnPandas is particularly recommended for data scientists, analysts, and engineers who need to perform data cleaning, transformation, and analysis as part of their work. It is also suitable for academics and researchers dealing with data in various formats and needing powerful tools for their data-driven research.
Huginn is highly recommended for developers, IT professionals, and hobbyists who enjoy tinkering with technology. It's also suitable for organizations looking to automate specific data collection or monitoring tasks and who have the technical expertise required to implement and manage such systems.
Based on our record, Pandas should be more popular than Huginn. It has been mentiond 219 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Libraries for data science and deep learning that are always changing. - Source: dev.to / about 1 month ago
# Read the content of nda.txt Try: Import os, types Import pandas as pd From botocore.client import Config Import ibm_boto3 Def __iter__(self): return 0 # @hidden_cell # The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials. # You might want to remove those credentials before you share the notebook. Cos_client = ibm_boto3.client(service_name='s3', ... - Source: dev.to / about 2 months ago
As with any web scraping or data processing project, I had to write a fair amount of code to clean this up and shape it into a format I needed for further analysis. I used a combination of Pandas and regular expressions to clean it up (full code here). - Source: dev.to / 2 months ago
Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 4 months ago
This tutorial provides a concise and foundational guide to exploring a dataset, specifically the Sample SuperStore dataset. This dataset, which appears to originate from a fictional e-commerce or online marketplace company's annual sales data, serves as an excellent example for learning and how to work with real-world data. The dataset includes a variety of data types, which demonstrate the full range of... - Source: dev.to / 10 months ago
Https://n8n.io/, https://github.com/huginn/huginn, https://automatisch.io/, https://www.activepieces.com/ and theres a lot more... I've used n8n, node-red, and huginn (a while back), but imo n8n has been the simplest off the shelf. - Source: Hacker News / over 1 year ago
The device itself is really cute. I'm not sure about handing oauth tokens to all my accounts to a third party for them to run huginn/selenium on a backend that might not be online for more than a year. I'm barely comfortable with Alexa having a connection to my iTunes for podcasts. What happens when Uber or whoever decides to throw a captcha between Rabbit and the web frontend? I'd like to see it do more than help... - Source: Hacker News / over 1 year ago
I skipped to chapter 9 in the article ("Clogged"), and it looked like Pipes failed because it didn't have a large enough team or a well-defined mission. As a result they couldn't offer a super robust product that would lure in enterprise users. "You could not purchase some number of guaranteed-to-work Pipes calls per month" is the quote from the article. The reason I think that interesting is because that's the... - Source: Hacker News / over 1 year ago
"correct" is a value judgement that depends on lots of different things. Only you can decide which tool is correct. Here are some ideas: - https://camel.apache.org/ - https://www.windmill.dev/ Your idea about a queue (in redis, or postgres, or sqlite, etc) is also totally valid. These off-the-shelf tools I listed probably wouldn't give you a huge advantage IMO. - Source: Hacker News / over 1 year ago
Huginn (https://github.com/huginn/huginn) has like some 39K stars on Github and the use cases it covered looks good. Source: almost 2 years ago
NumPy - NumPy is the fundamental package for scientific computing with Python
n8n.io - Free and open fair-code licensed node based Workflow Automation Tool. Easily automate tasks across different services.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.
OpenCV - OpenCV is the world's biggest computer vision library
Zapier - Connect the apps you use everyday to automate your work and be more productive. 1000+ apps and easy integrations - get started in minutes.
