Software Alternatives, Accelerators & Startups
Register | Login

LabPlot VS Scikit-learn

Compare LabPlot VS Scikit-learn and see what are their differences

DocRaptor
As the only API powered by the Prince HTML-to-PDF engine, DocRaptor provides the best support for complex PDFs with powerful support for headers, page breaks, page numbers, flexbox, watermarks, accessible PDFs, and much more featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

LabPlot logo LabPlot

LabPlot is a KDE-application for interactive graphing and analysis of scientific data.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • LabPlot
    Image date //
    2024-09-02
  • LabPlot
    Image date //
    2024-09-02
  • LabPlot
    Image date //
    2024-09-02
  • LabPlot
    Image date //
    2024-09-02
  • LabPlot
    Image date //
    2024-09-02
  • LabPlot
    Image date //
    2024-09-02
  • LabPlot
    Image date //
    2024-09-05
  • LabPlot
    Image date //
    2024-09-05
  • LabPlot
    Image date //
    2024-09-05

LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone and trusted by professionals.

FEATURE HIGHLIGHTS

  • High-quality data visualization and interactive plotting
  • Data analysis, statistics, nonlinear regression, curve and peak fitting
  • Fast computing with interactive notebooks (for Python, R, Julia, Maxima and more)
  • Data extraction from images (Plot Digitizer)
  • Smooth data import and export to and from multiple formats (CSV, JSON, ODS, XLSX, Origin, SAS, Stata, SPSS, MATLAB, SQL, MQTT, BLF, HDF5, FITS, ROOT (CERN), LTspice, Ngspice and more)
  • Available for Windows, macOS, Linux, FreeBSD, Haiku, GNU

A full list of features: https://labplot.kde.org/features

Video tutorials: https://www.youtube.com/@LabPlot

Communication channels: https://labplot.kde.org/support

Get it here: https://labplot.kde.org/download

  • Scikit-learn Landing page
    Landing page //
    2022-05-06

LabPlot features and specs

  • Open Source
    LabPlot is free and open source, allowing users to modify and distribute the software without any cost.
  • Integration with KDE
    LabPlot is part of the KDE software collection, offering seamless integration with other KDE applications and a consistent look and feel.
  • Multiplatform Support
    LabPlot is available on various platforms, including Linux, Windows, and macOS, making it accessible to a wide range of users.
  • Extensive Plotting Features
    LabPlot offers a wide range of plotting capabilities, including 2D and 3D plots, which can accommodate diverse scientific and engineering needs.
  • Customizability
    Users can customize plots extensively in LabPlot, adjusting parameters such as plot style, color, and data presentation to suit their specific needs.

Possible disadvantages of LabPlot

  • Steeper Learning Curve
    Due to its comprehensive features, new users might find LabPlot challenging to learn and may require time to become proficient.
  • Limited Community Support
    While there is a community around LabPlot, the size is relatively small compared to more widely used plotting tools, potentially limiting peer support.
  • Performance Issues with Large Datasets
    LabPlot may experience performance slowdowns when handling very large datasets, which can hinder productivity for users working with such data.
  • Less Frequent Updates
    LabPlot may receive updates less frequently than some commercial software, possibly affecting the pace of new feature integration.

Scikit-learn features and specs

  • Ease of Use
    Scikit-learn provides a high-level interface for common machine learning algorithms, making it easy for beginners and professionals to implement complex models with minimal coding.
  • Extensive Documentation and Community Support
    The library has comprehensive documentation and a large, active community. This makes it easy to find tutorials, examples, and solutions to common problems.
  • Integration with Other Libraries
    Scikit-learn integrates well with other scientific computing libraries such as NumPy, SciPy, and pandas, allowing for seamless data manipulation and analysis.
  • Variety of Algorithms
    It offers a wide array of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
  • Performance
    Designed with performance in mind, many of the algorithms are optimized and some even support multicore processing.

Possible disadvantages of Scikit-learn

  • Limited Deep Learning Support
    Scikit-learn is primarily focused on traditional machine learning algorithms and does not offer support for deep learning models, unlike libraries like TensorFlow or PyTorch.
  • Not Ideal for Large-Scale Data
    While Scikit-learn performs well for moderate-sized datasets, it may not be the best choice for extremely large datasets or big data applications.
  • Lack of Online Learning Algorithms
    The library has limited support for online learning algorithms, which are useful for scenarios where data arrives in a stream and model needs to be updated incrementally.
  • Less Flexibility in Customization
    It can be less flexible compared to lower-level libraries when highly customized or specific implementations are needed.
  • Dependency Overhead
    Scikit-learn relies on several other Python libraries like NumPy and SciPy, which might require users to manage multiple dependencies.

LabPlot videos

+ Add

How to fit a curve using LabPlot

More videos:

  • Tutorial - Quick Statistics and Visual Overview of Data in LabPlot
  • Tutorial - How to export publication-quality plots from LabPlot
  • Tutorial - Your First Data Import and Visualization in LabPlot

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

  • Review - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

Category Popularity

0-100% (relative to LabPlot and Scikit-learn)

LabPlot

Scikit-learn

Technical Computing
100 100%
Technical Computing
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Numerical Computation
100 100%
Numerical Computation
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using LabPlot and Scikit-learn. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare LabPlot and Scikit-learn

LabPlot Reviews

  1. Mark

    LabPlot provides extensive capabilities for data import and export, along with tools for analysis, curve fitting, nonlinear regression and interactive visualization, including live data support. Users can export graphs in various formats and utilize a built-in plot digitizer to extract data from existing charts. Additionally, if users are familiar with programming languages such as Python or R, they can leverage these within LabPlot's interactive notebooks.

Scikit-learn Reviews

15 data science tools to consider using in 2021
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...
Source: searchbusinessanalytics.techtarget.com

Social recommendations and mentions

Based on our record, Scikit-learn seems to be more popular. It has been mentiond 31 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.

LabPlot mentions (0)

We have not tracked any mentions of LabPlot yet. Tracking of LabPlot recommendations started around Mar 2021.

Scikit-learn mentions (31)

  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    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 / 3 months ago
  • 🚀 Launching a High-Performance DistilBERT-Based Sentiment Analysis Model for Steam Reviews 🎮🤖
    Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 5 months ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 11 months ago
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / about 1 year ago
  • Link Prediction With node2vec in Physics Collaboration Network
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / almost 2 years ago
View more

What are some alternatives?

When comparing LabPlot and Scikit-learn, you can also consider the following products

SciDaVis - SciDAVis is a free application for Scientific Data Analysis and Visualization.

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

GnuPlot - Gnuplot is a portable command-line driven interactive data and function plotting utility.

OpenCV - OpenCV is the world's biggest computer vision library

DataMelt - DataMelt (DMelt), a free mathematics and data-analysis software for scientists, engineers and students.

NumPy - NumPy is the fundamental package for scientific computing with Python

Loading...