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Python libraries for data visualisation, data analysis and scientific computing

13 May 2016 No Comment

Python libraries for data visualisation, data analysis and scientific computing mainly used for Big Data.

Libraries for Data Visualization

  • matplotlib – A Python 2D plotting library.
  • bokeh – Interactive Web Plotting for Python.
  • ggplot – Same API as ggplot2 for R.
  • plotly – Collaborative web plotting for Python and matplotlib.
  • pygal – A Python SVG Charts Creator.
  • pygraphviz – Python interface to Graphviz.
  • PyQtGraph – Interactive and realtime 2D/3D/Image plotting and science/engineering widgets.
  • SnakeViz – A browser based graphical viewer for the output of Python’s cProfile module.
  • vincent – A Python to Vega translator.
  • VisPy – High-performance scientific visualization based on OpenGL.

Libraries for Scientific Computing and Data Analysis

  • astropy – A community Python library for Astronomy.
  • bcbio-nextgen – A toolkit providing best-practice pipelines for fully automated high throughput sequencing analysis.
  • bccb – Collection of useful code related to biological analysis.
  • Biopython – Biopython is a set of freely available tools for biological computation.
  • blaze – NumPy and Pandas interface to Big Data.
  • cclib – A library for parsing and interpreting the results of computational chemistry packages.
  • NetworkX – A high-productivity software for complex networks.
  • Neupy – Running and testing different Artificial Neural Networks algorithms.
  • NIPY – A collection of neuroimaging toolkits.
  • Numba – Python JIT (just in time) complier to LLVM aimed at scientific Python by the developers of Cython and NumPy.
  • NumPy – A fundamental package for scientific computing with Python.
  • Open Babel – A chemical toolbox designed to speak the many languages of chemical data.
  • Open Mining – Business Intelligence (BI) in Python (Pandas web interface)
  • orange – Data mining, data visualization, analysis and machine learning through visual programming or Python scripting.
  • Pandas – A library providing high-performance, easy-to-use data structures and data analysis tools.
  • PyDy – Short for Python Dynamics, used to assist with workflow in the modeling of dynamic motion based around NumPy, SciPy, IPython, and matplotlib.
  • PyMC – Markov Chain Monte Carlo sampling toolkit.
  • RDKit – Cheminformatics and Machine Learning Software.
  • SciPy – A Python-based ecosystem of open-source software for mathematics, science, and engineering.
  • statsmodels – Statistical modeling and econometrics in Python.
  • SymPy – A Python library for symbolic mathematics.
  • zipline – A Pythonic algorithmic trading library.

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