Pandas 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.
Based on our record, Pandas seems to be a lot more popular than DocFX. While we know about 219 links to Pandas, we've tracked only 7 mentions of DocFX. 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 / about 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 / 9 months ago
This is a better looking version of what Java and C# have had for a long time (kudos to the author for that!), is that the inspiration for this tool? https://docs.oracle.com/javase/8/docs/technotes/tools/windows/javadoc.html https://dotnet.github.io/docfx/ I saw the author mentioned in another comment that they found themselves peeping inside type declaration files "too often". While I do often use sites generated... - Source: Hacker News / over 1 year ago
Actually, we use it for OptiTune, it's called "docfx" https://dotnet.github.io/docfx/. Source: over 3 years ago
We would really prefer to use a somewhat generic pre-made tool for this (such as DocFX) compared to rolling our own solution. We can roll our own solution... But would prefer not to so that we can minimize development and maintenance overhead. Source: over 3 years ago
I use docfx from microsoft to generate documentation for all my oss libraries. Source: over 3 years ago
My best guess would be that there's a CI/CD pipeline in GitHub that utilizes DocFX to convert the Markdown files to HTML. The constructed HTML files are then placed in an Azure Storage account that configured for Static Website Hosting combined with Azure CDN. Source: over 3 years ago
NumPy - NumPy is the fundamental package for scientific computing with Python
Doxygen - Generate documentation from source code
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Natural Docs - Natural Docs is an open-source documentation generator for multiple programming languages.
OpenCV - OpenCV is the world's biggest computer vision library
JSDoc - An API documentation generator for JavaScript.