10 most extensively used Python libraries

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10 most extensively used Python libraries
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(No Transcript)
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• Python, being one of the most sought after
  programming languages, has a huge collection of
  libraries. In fact, this expansive set of
  libraries can be considered as one of the
  important merits and reasons for its popularity.
  Python has over 137,000 libraries and 198,826
  packages for the ease of coders regular coding
  experience.
• We have collated a list of 10 most extensively
  used Python libraries here
• OS
• Requests
• SQLAlchemy
• Numpy
• Tensorflow Keras
• Matplotlib
• Celery
• OpenCV
• Sci-kit learn
• Pandas

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• Os
• Python, being one of the most sought after
  programming languages, has a huge collection of
  libraries. In fact, this expansive set of
  libraries can be considered as one of the
  important merits and reasons for its popularity.
  Python has over 137,000 libraries and 198,826
  packages for the ease of coders regular coding
  experience.
• Requests
• The requests library is the most preferred way to
  deal with the Http request. It conceals the
  complexities of making requests behind a simple
  module. Its an appropriate library for consuming
  data and helps the user to focus on services and
  logical components.
• SQLAlchemy
• SQLAlchemy, released in February 2006 is a
  popular Object Relational Mapper tool. It gives
  good control and flexibility over SQL to a
  developer. Distributed under MIT license,
  SQLAlchemy is open-source and platform-independent
  .SQLAlchemy efficiently, maps classes to the
  database, hence it is a good choice for
  developing clean object models and database
  schema.

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• Numpy
• An array-processing package, which provides a
  systematic way of handling multidimensional array
  object. It is the fundamental module for
  scientific calculations with Python.
• Apart from its scientific utility, we can also
  use it as a container for multidimensional data
  for efficient data processing.
• We can use the List for handling multidimensional
  data but Numpy is more desirable because
• it occupies less memory as compared to the list.
• its faster than the list.
• Tensorflow Keras
• Python has gained a lot of fame for its role in
  various fields like Data Science and Machine
  learning. Tensorflow and Keras are the most
  prominent library used in production for deep
  learning models.
• Keras is a high-level API developed on TensorFlow
  which is more user-friendly and simple to code as
  compared to TF. Its handy to use Keras when we
  want to quickly build and test a neural network
  with decent lines of code. The Model and the
  Sequential APIs are so robust that you can do
  almost everything you may want.

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• Why use TensorFlow?
• As per its official website, TensorFlow is an
  end-to-end open-source platform for machine
  learning.
• Unlike Keras, TensorFlow offers more high-level
  operations. Its handy if you are doing research
  or working on some special kind of deep learning
  models.
• It provides more Flexibility than Keras if you
  want to define something of your own or new, TF
  would be the best choice. It is more advanced and
  provides more control over your network.
• Matplotlib
• Visual aids are more understandable and readable
  in order to give relevant insight regarding the
  subject. Hence its really important to represent
  our observation on the graph or by some visible
  patterns or charts.
• Matplotlib is a Python two-dimensional plotting
  library which produces production level figures
  in various kinds of formats and interactive
  environments across platforms.

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• Celery
• Celery helps in the unsynchronized execution of
  any module independent of HTTP requests or
  responses. Its easy to maintain celery and it
  doesnt require and configuration file. Celery
  supports features like
• it occupies less memory as compared to the list.
• its faster than the list.
• OpenCV-Python
• Scikit-learn is an open-source Python library
  that fulfills a variety of machine learning,
  preprocessing, cross-validation and visualization
  algorithms using a combined interface.
• Easy and productive tools for predictive data
  interpretation.
• Available to everybody, and reusable in various
  contexts.
• Built on NumPy, SciPy, and matplotlib libraries.
• Open source, commercially usable BSD license.

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• It contains various important Machine learning
  algorithms prebuild in form of sub-packages, such
  as
• Classification SVM, Nearest neighbours, Random
  forest.
• RegressionSVM,K-nearest neighbors,Random forest.
• Clustering K-Means, Spectral clustering, Mean
  shift.
• Pandas
• Pandas is regarded as one of the most important
  and popular libraries, as it provides fast,
  flexible, and powerful data structures intended
  to operate with relational or labelled data. The
  most obvious objective of Pandas is to be the
  primary high-level module for doing practical and
  real-world data analysis. It has the broader
  purpose of becoming the most dominant and
  flexible open-source data analysis tool available
  in any language.

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Conclusion
Python has an exceptional variety of libraries,
from NumPy and scikit-learn for experimental
computing to Django for web development. There
are some libraries that specifically focus on
certain particular tasks like nltk for natural
language processing and Beautiful Soup, for web
scraping to gather data from HTML and XML files
or webpages. There are libraries focus on solving
a definite problem, like scikit-learn for machine
learning applications and nltk for natural
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