Pandas
NumPy
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
WEKA
Google Keep
OneNote
Evernote
Joplin
Standard Notes
Simplenote
Todoist
Notion
Pandas Google KeepPandas 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.
Google Keep is recommended for individuals who need a basic, user-friendly note-taking tool without excess features. It is particularly beneficial for users who are frequent users of other Google services, as it offers seamless integration. It's an ideal choice for students, professionals, or anyone needing to keep quick, organized notes and lists.
Based on our record, Pandas seems to be more popular. 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 / 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
NumPy - NumPy is the fundamental package for scientific computing with Python
OneNote - Get the OneNote app for free on your tablet, phone, and computer, so you can capture your ideas and to-do lists in one place wherever you are. Or try OneNote with Office for free.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Evernote - Bring your life's work together in one digital workspace. Evernote is the place to collect inspirational ideas, write meaningful words, and move your important projects forward.
OpenCV - OpenCV is the world's biggest computer vision library
Joplin - Joplin is a free, open source note taking and to-do application, which can handle a large number of notes organised into notebooks. The notes are searchable, tagged and modified either from the applications directly or from your own text editor.