Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
DataMelt, or "DMelt", is an environment for numeric computations, data analysis, data mining and computational statistics. DataMelt allows you to plot functions and data in 2D or 3D, perform statistical testing, data mining, data analysis, numeric computations and function minimization. It also solves systems of linear and differential equations. There are also options for symbolic, non-linear, and linear regression. Java API integrates neural networks and data-manipulation techniques using various data-manipulation algorithms. Support is provided for elements of symbolic computations using Octave/Matlab programming.
DataMelt provides a Java platform-based computational environment. It can be used on different operating systems and programming languages. It is not limited to one programming language, unlike other statistical programs. This software combines Java, the most widely used enterprise language in the world, with the most popular data science scripting languages, Jython (Python), Groovy and JRuby.
Description
Pandas is an open-source data analysis and manipulation tool that is not only fast and powerful but also highly flexible and user-friendly, all within the Python programming ecosystem. It provides various tools for importing and exporting data across different formats, including CSV, text files, Microsoft Excel, SQL databases, and the efficient HDF5 format. With its intelligent data alignment capabilities and integrated management of missing values, users benefit from automatic label-based alignment during computations, which simplifies the process of organizing disordered data. The library features a robust group-by engine that allows for sophisticated aggregating and transforming operations, enabling users to easily perform split-apply-combine actions on their datasets. Additionally, pandas offers extensive time series functionality, including the ability to generate date ranges, convert frequencies, and apply moving window statistics, as well as manage date shifting and lagging. Users can even create custom time offsets tailored to specific domains and join time series data without the risk of losing any information. This comprehensive set of features makes pandas an essential tool for anyone working with data in Python.
API Access
Has API
API Access
Has API
Integrations
3LC
Amazon SageMaker Data Wrangler
ApertureDB
Avanzai
Cleanlab
DagsHub
Dagster
Dash
Eclipse BIRT
Flower
Integrations
3LC
Amazon SageMaker Data Wrangler
ApertureDB
Avanzai
Cleanlab
DagsHub
Dagster
Dash
Eclipse BIRT
Flower
Pricing Details
$0
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
jWork.ORG
Founded
2005
Country
United States
Website
datamelt.org
Vendor Details
Company Name
pandas
Founded
2008
Website
pandas.pydata.org
Product Features
Artificial Intelligence
Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)
Data Analysis
Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics
Data Mining
Data Extraction
Data Visualization
Fraud Detection
Linked Data Management
Machine Learning
Predictive Modeling
Semantic Search
Statistical Analysis
Text Mining
Data Visualization
Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
Statistical Analysis
Analytics
Association Discovery
Compliance Tracking
File Management
File Storage
Forecasting
Multivariate Analysis
Regression Analysis
Statistical Process Control
Statistical Simulation
Survival Analysis
Time Series
Visualization
Product Features
Data Analysis
Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics