Usually, a library is a collection of media, books, or materials that are accessible for use and not just for display purposes. Similarly, in the programming sphere, a library is a collection of precompiled codes that can be used later in the program for well-set operations. Not just the pre-compiled codes, libraries may contain message templates, configuration data, documentation, etc.
Python libraries are a collection of related modules that contains a package of code used repeatedly in varied programs. This makes Python programming easier and simpler as we don’t require writing codes from the scratch. With the support of around 137,000 libraries, Python is the most favored programming language to create applications and models in varied fields.
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Top Python Libraries
Now that we are well aware that what the libraries are, let us quickly dig deep into the Top 5 Python libraries to learn in the year 2022 and beyond.
Pandas is an open-source library used widely for data processing, cleaning, and evaluation datasets. It is one of the leading foundational libraries that allow the users to create relational or labeled Pandas data structures for modeling, statistical analysis, manipulation, and data cleaning. Pandas assist several operations like Re-indexing, sorting, aggregations, visualizations, data conversions, sorting, iterations, and more.
Features of Pandas Library:
- It offers data frames and series that enable us to represent and manipulate the data in several ways. It’s widely cherished for its proficient way to manage and explore data.
- Pandas are backed by intelligent alignment and indexing of data. One can easily label and organize the data thanks to alignment and indexing methods, found exclusively in Pandas.
- It is believed to have highly optimized performance. The critical codes are written using Cython or C thereby making it highly fast and responsive.
SciPy stands for ‘Scientific Python’. It’s an open-source Python library used for statistical modeling and other high levels of scientific computations. It is based on NumPy extension that enables developers to process and visualize data and other high-level commands. SciPy’s major goal is to offer potent N-dimensional array manipulation and processing capabilities. Owing to this, developers can effortlessly implement ML algorithms without getting into complicated stats or maths.
Features of SciPy Library:
- SciPy library contains several sub-packages to assist in fixing issues about Scientific Computation
- It can run on an array of NumPy library
- SciPy is simple, easy to use, and has superior computational power
NumPy stands for ‘Numerical Python. This library is used for scientific calculations and features an inbuilt mathematical function for quick computation. It supports multi-dimensional data and large matrices. Few important functions of NumPy include radians(), arccos(),arcsin(), tan(), and more. It provides a potent N-dimensional array with rows and columns for effective processing and manipulation capabilities. It is also known to boosts the speed of big Pandas data sets as it does the calculations with array objects in Cython. It is open-source, faster, and easy to use.
Features of NumPy:
- NumPy contains tools to integrate the codes from Fortran C++ and C
- NumPy provides a high-performance N-dimensional array for efficient manipulation and processing. It can be both one dimensional and multi-dimensional
- It is capable to perform intricate operations including Fourier transform, linear algebra, and more.
TensorFlow is an open-source software library for conducting computations based on numerics by utilizing data flow charts. TensorFlow is a flexible ecosystem comprising libraries, community resources, and tools to allow developers to build and deploy Machine Learning powered applications with ease. It is fast and employs techniques like Accelerated Linear Algebra or XLA to do linear algebra computations within split seconds. Owing to its flexible ecosystem, one can deploy computation to one or more GPUs or CPUs in the server or desktop without writing the code.
Features of TensorFlow:
- It helps to view the graph at each node. Tensor Board works by visualizing with computational graphing methods and provides a dashboard where you can see the evaluation of each element.
- We can run TensorFlow applications on a variety of supported hardware solutions such as Clouds, Android, and architecture including GPUs and CPUs. This means the framework can execute on several embedded hardware platforms.
- TensorFlow offers a wide variety of toolkits to resolve complex neural networks. It supports the development of intelligent object-oriented models using easy prototyping with Keras API.
Scikit-Learn is another library you should consider learning in the year 2022. It is an open-source library that supports machine learning and data science. One can learn many kinds of data science and machine learning algorithms like support vector machines, clustering models, and more. It integrates closely with SciPy and Numpy and is revered as one of the leading libraries to work with complex data.
Features of Scikit-Learn:
- One can extract features from images and text using Scikit-Learn
- Validation and accuracy of supervised models on unseen data can be cross-validated with the help of Scikit-Learn
- The clustering feature enables easy grouping of unlabeled data
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