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RAWGraphs VS NumPy

Compare RAWGraphs VS NumPy and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

RAWGraphs logo RAWGraphs

RAWGraphs is an open source app built with the goal of making the visualization of complex data...

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • RAWGraphs Landing page
    Landing page //
    2022-06-16
  • NumPy Landing page
    Landing page //
    2023-05-13

RAWGraphs features and specs

  • User-Friendly Interface
    RAWGraphs provides an intuitive drag and drop interface, making it accessible for users with various technical skills.
  • Open Source
    Being open source, RAWGraphs allows for customization and community contributions, promoting transparency and flexibility.
  • Supports Multiple Data Formats
    RAWGraphs supports a variety of input formats like CSV, TSV, JSON, etc., enhancing its adaptability to different data sources.
  • Extensive Visualization Types
    Offers a wide range of visualization types such as bar graphs, scatter plots, and network graphs, catering to diverse analytical needs.
  • No Installation Required
    As a web-based tool, it does not require any installation, making it easy to access and use anywhere with an internet connection.
  • Export Options
    Allows exporting visualizations in vector (SVG) and raster (PNG) formats, which is valuable for high-quality reporting and presentations.

Possible disadvantages of RAWGraphs

  • Limited Interactivity
    Visualizations created with RAWGraphs are generally static, lacking advanced interactive features found in other tools.
  • Performance with Large Datasets
    May struggle with performance issues when handling very large datasets, which can limit its use for extensive data analytics.
  • Learning Curve for Advanced Features
    While basic functionalities are user-friendly, leveraging advanced features and customizations may require a steeper learning curve.
  • Dependency on Internet
    As a web-based application, it requires an internet connection to function, which can be a limitation in restricted or offline environments.
  • Limited Data Manipulation
    Provides basic data manipulation features, but lacks the depth and complexity available in specialized data processing tools.
  • Support and Documentation
    As an open-source project, it may not have the extensive support and documentation available with commercial visualization tools.

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

Analysis of RAWGraphs

Overall verdict

  • Yes, RAWGraphs is a good tool for creating data visualizations due to its ease of use, versatility, and robust support for different data types and outputs.

Why this product is good

  • RAWGraphs is considered a good data visualization tool because it is open-source, versatile, and easy to use. It allows users to create a wide variety of charts and visualizations without needing extensive coding knowledge. Its interface is intuitive and facilitates the quick transformation of data sets into visually compelling graphics. Furthermore, it supports multiple formats for data input and export, making it flexible for various project needs.

Recommended for

  • data analysts
  • journalists
  • researchers
  • educators
  • students
  • designers who need to create visualizations without in-depth coding skills.

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

RAWGraphs videos

+ Add

RawGraphs Walkthrough

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Category Popularity

0-100% (relative to RAWGraphs and NumPy)

RAWGraphs

NumPy

Data Visualization
100 100%
Data Visualization
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Charting Libraries
100 100%
Charting Libraries
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare RAWGraphs and NumPy

RAWGraphs Reviews

Top 10 Tableau Open Source Alternatives: A Comprehensive List
RAWGraphs is an open-source Data Visualization tool designed to make visualizing complex data simple for everyone. The primary goal of RAWGraphs is to provide a tool that allows people who do not have the technical/coding expertise to create visualizations on their own. Originally designed to help graphic designers complete a set of tasks that were not available in other...
Source: hevodata.com

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than RAWGraphs. While we know about 122 links to NumPy, we've tracked only 5 mentions of RAWGraphs. 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.

RAWGraphs mentions (5)

  • Interview synthesis tools?
    Go back through a second time Code themes / pull insights/ double check for keywords tag accuracy Use Dovetailโ€™s โ€œchartsโ€ to review various tags (it will show you how many tags per word in various chart options, none are great.) Export desired csvโ€™s from Dovetail Charts to free online data viz software like https://rawgraphs.io Boom. Iโ€™m sure there are better ways but thatโ€™s what I got! Source: over 4 years ago
  • What type/style of chart is this?
    Sankey is probably the most common name (after Captain Matthew Henry Phineas Riall Sankey who apparently made them to study energy flows in steam engines). But I've also heard it referred to as an alluvial diagram, for example in https://rawgraphs.io/. Source: over 4 years ago
  • Show HN: I made a data visualization desktop app
    This seems quite similar to RawGraphs: https://rawgraphs.io/ Both seem to provide a similar interface for dragging in a CSV file and constructing a chart, but RawGraphs is open-source, and can be used in the browser without installing anything (or the code can be downloaded and served locally). The main advantage of Daigo over RawGraphs seems to be that it supports publishing multiple charts as a dashboard.... - Source: Hacker News / over 4 years ago
  • [OC] Latin Americaโ€™s biggest airports had been growing steadily. With Covid, it all changed.
    Tools: Excel, Rawgraphs, Affinity Designer. Source: over 4 years ago
  • Self-hosted solution for easy data visualization?
    Take a look at https://rawgraphs.io/. Source: over 5 years ago

NumPy mentions (122)

View more

What are some alternatives?

When comparing RAWGraphs and NumPy, you can also consider the following products

Plotly - Low-Code Data Apps

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

D3.js - D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Tableau - Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.

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

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