DataMelt VS Pandas
Compare DataMelt VS Pandas and see what are their differences
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives.
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DataMelt
SciDaVis
LabPlot
Aveloy Graph
RJS Graph
IGOR Pro
DPlot
OriginPro
Pandas
NumPy
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
WEKA
DataMelt is a Java program for statistics, general data analysis and data visualization. The program is often termed "computational platform" since it can be used with different programming languages (Java, Python, Groovy..). DataMelt is not limited to a single programming language. The program is used for numeric computation, statistics, analysis of large data volumes ("big data") and scientific visualization. Full description: https://handwiki.org/wiki/Software:DataMelt
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students and data scientists
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DataMelt has its roots in particle physics where data mining is a primary task. It was created as Software:jHepWork project in 2005 and it was initially written for data analysis for particle physics.
DataMelt's answer
Multiplatform. Supports multiple programming languages: Java, Python (Jython), Groovy, Ruby
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Large database of examples and code snippets https://datamelt.org/code/
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Students at universities and data scientists.
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Java (JDK any new new release including JDK20)
I like this DataMelt analysis program since it has many 2D/3D visualisation and a massive number of practical examples
Based on our record, Pandas seems to be more popular. It has been mentiond 198 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.
Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience. - Source: dev.to / 14 days ago
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method. - Source: dev.to / 8 days ago
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail.... - Source: dev.to / 2 months ago
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts. - Source: dev.to / 5 months ago
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks. - Source: dev.to / 5 months ago
SciDaVis - SciDAVis is a free application for Scientific Data Analysis and Visualization.
NumPy - NumPy is the fundamental package for scientific computing with Python
LabPlot - LabPlot is a KDE-application for interactive graphing and analysis of scientific data.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Aveloy Graph - Aveloy Graph is an application for graph creation / data visualization
OpenCV - OpenCV is the world's biggest computer vision library