10 most extensively used Python libraries

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 coder’s regular coding experience.

We have collated a list of 10 most extensively used Python libraries here:

• Os

  The Os library is an inbuilt feature of Python. It works as a medium between the system os and the code. It’s a frequently used module as it works as an interface between the code and the operating system.

• 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. It’s 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.

• 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.
  • it’s 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. It’s 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.

  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. It’s 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.

• Celery

  Celery helps in the unsynchronized execution of any module independent of HTTP requests or responses. It’s easy to maintain celery and it doesn’t require and configuration file. Celery supports features like :

  • it occupies less memory as compared to the list.
  • it’s faster than the list.

• OpenCV-Python

  Python can easily be extended from C or C++ language. Being slow while processing as compared to C or C++, various rigorous codes can be generated by extending C or C++ into Python modules as in the case of OpenCV.In short, Python-OpenCV is just a wrapper around C and C++ code i.e OpenCV is rightfully written in C and C++.
  Advantage of extending OpenCV as Python module as following merits:

  • the code remains as fast as the original C and C++ code (since the C++ code working in the background).
  • its easier to code in Python than C and C++.

• Scikit-learn

  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.

  It contains various important Machine learning algorithms prebuild in form of sub-packages, such as:

  • Classification: SVM, Nearest neighbours, Random forest.
  • Regression:SVM,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.

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 language processing.

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