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Top 6 Python Libraries for Developers


Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming.There are a lots of python libraries available in the internet.

Here is a list of 10 best Python Libraries for your dynamic works..

Top 6 Python Libraries for Developers


Retrying is an Apache 2.0 licensed general-purpose retrying library, written in Python, to simplify the task of adding retry behavior to just about anything.
  • Generic Decorator API
  • Specify stop condition (i.e. limit by number of attempts)
  • Specify wait condition (i.e. exponential backoff sleeping between attempts)
  • Customize retrying on Exceptions
  • Customize retrying on expected returned result


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Requests is the only Non-GMO HTTP library for Python, safe for human consumption.


  • International Domains and URLs
  • Keep-Alive & Connection Pooling
  • Sessions with Cookie Persistence
  • Browser-style SSL Verification
  • Basic/Digest Authentication
  • Elegant Key/Value Cookies
  • Automatic Decompression
  • Automatic Content Decoding
  • Unicode Response Bodies


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Jupyter notebook is the language-agnostic HTML notebook application for Project Jupyter. In 2015, Jupyter notebook was released as part of The Big Split™ of the IPython codebase. IPython 3 was the last major monolithic release containing both language-agnostic code, such as the IPython notebook, and language specific code, such as the IPython kernel for Python.


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pyglet: a cross-platform windowing and multimedia library for Python, pyglet provides an object-oriented programming interface for developing games and other visually-rich applications for Windows, Mac OS X and Linux.


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NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.


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Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora.


  • All algorithms are memory-independent w.r.t. the corpus size (can process input larger than RAM, streamed, out-of-core),
  • Intuitive interfaces
    • easy to plug in your own input corpus/datastream (trivial streaming API)
    • easy to extend with other Vector Space algorithms (trivial transformation API)
  • Efficient multicore implementations of popular algorithms, such as online Latent Semantic Analysis (LSA/LSI/SVD),Latent Dirichlet Allocation (LDA), Random Projections (RP), Hierarchical Dirichlet Process (HDP) or word2vec deep learning.


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