Matplotlib

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NVD3

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Matplotlib vs NVD3: What are the differences?

Developers describe Matplotlib as "A plotting library for the Python programming language". It is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. It can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits. On the other hand, NVD3 is detailed as "Re-usable charts for d3.js". This project is an attempt to build re-usable charts and chart components for d3.js without taking away the power that d3.js gives you. This is a very young collection of components, with the goal of keeping these components very customizable, staying away from your standard cookie cutter solutions.

Matplotlib and NVD3 can be categorized as "Charting Libraries" tools.

NVD3 is an open source tool with 6.93K GitHub stars and 2.23K GitHub forks. Here's a link to NVD3's open source repository on GitHub.

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      What is Matplotlib?

      It is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. It can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.

      What is NVD3?

      This project is an attempt to build re-usable charts and chart components for d3.js without taking away the power that d3.js gives you. This is a very young collection of components, with the goal of keeping these components very customizable, staying away from your standard cookie cutter solutions.
      What companies use Matplotlib?
      What companies use NVD3?

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      What tools integrate with Matplotlib?
      What tools integrate with NVD3?

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      What are some alternatives to Matplotlib and NVD3?
      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.
      MATLAB
      Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.
      Bokeh
      Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets.
      R Language
      R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
      Plotly.js
      It is a standalone Javascript data visualization library, and it also powers the Python and R modules named plotly in those respective ecosystems (referred to as Plotly.py and Plotly.R). It can be used to produce dozens of chart types and visualizations, including statistical charts, 3D graphs, scientific charts, SVG and tile maps, financial charts and more.
      See all alternatives
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