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 should be more popular than aider. It has been mentiond 220 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.
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโฆ. - Source: dev.to / 14 days ago
Libraries for data science and deep learning that are always changing. - Source: dev.to / 5 months 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 / 6 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 / 6 months ago
Pythonโs 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 youโre experienced or just starting, Pythonโs clear style makes it a good choice for diving into machine learning. Actionable Tip: If youโre new to Python,... - Source: dev.to / 8 months ago
I just started using aider, recommend it: https://aider.chat/ It indexes files in your repo, but you can control which specific files to include when prompting and keep it very limited/controlled. - Source: Hacker News / 12 days ago
Aider and Goose are also open source. Goose is backed by a big company, but Aider isn't and was one of the first (that I know of at least). https://aider.chat/ https://block.github.io/goose/. - Source: Hacker News / about 2 months ago
Feels very similar to Aider[1] 1: https://aider.chat/. - Source: Hacker News / 2 months ago
I also tried Aider, an open-source Python CLI agent. It installed via pip install aider-install and gave me an aider command to use anywhere. Aider stands out for flexibility: it supports 100+ languages and multiple LLMs, and it even shows token usage after each session. In practice, Aider automatically committed code changes and ran linters/tests after edits, which was handy for catching mistakes. It wasnโt as... - Source: dev.to / 2 months ago
Could really use a comparison versus the seemingly de-facto terminal AI coding tool Aider. https://aider.chat/. - Source: Hacker News / 3 months ago
NumPy - NumPy is the fundamental package for scientific computing with Python
GitHub Copilot - Your AI pair programmer. With GitHub Copilot, get suggestions for whole lines or entire functions right inside your editor.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Codebuff - Codebuff is a tool for editing codebases via natural language instruction to Mani, an expert AI programming assistant.
OpenCV - OpenCV is the world's biggest computer vision library
Sonnet - A new library for constructing neural networks from DeepMind