Python is a widely used general purpose programming language today. When it comes to handling data science tasks or challenges, Python never ceases to surprise its users. Data scientists leverage the power of Python programming language daily. It is easy to learn python, easy to debug, used everywhere, object-oriented, open-source, and a high-performance language.
Python has a set of inbuilt extraordinary Python libraries that are widely used by programmers to solve data science or machine learning-related tasks. Python libraries are the major reason that makes it popular among programmers worldwide.
The simplicity of programming language has attracted developers to create more libraries for machine learning and data science. Due to a vast collection of Python libraries, it has become immensely popular among data scientists and machine learning experts.
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The module is a file with some Python code and a package is a directory for modules or sub packages. There is only a minor difference between a package and a Python library, and it is quite blurred. A Python library is a reusable chunk of code that you can include into your projects. When compared to other programming languages like C or C++, Python libraries don’t pertain to a specific context in Python. A python library defines a collection of core modules. A package is a library that can be installed using package managers.
The Python Standard Library is a collection of tokens, syntax, or semantics in Python. It is bundled with core Python distribution and written in C programming language. It handles the core functionalities of core modules in Python. There are more than 200 core modules at the heart of the Python standard library. In addition to this, you may also access a growing collection of thousands of components from the Python Package Index.
It is a library for numeric computations to high precision with around 35K comments and a vibrant community of more than 1500 contributors. It is popular across all scientific fields. TensorFlow in simple terms can be defined as the framework to define or run computations that include tensors and eventually produce a value.
If you have worked on a machine learning project recently, then you must have heard of this library that was developed by Google in collaboration with Brain Team. It is utilized by almost every machine learning application released by Google.
TensorFlow is like a computational library for writing new algorithms that involve plenty of tensor operators. Further, neural networks can also be expressed as computational graphs and can be implemented by TensorFlow Python library. Additionally, Tensors are N-dimensional matrices that represent your data.
Features of TensorFlow:
It is a Python library optimized for the speed, and it uses techniques that are suitable quick algebra operations. Here are a few features of the library that you must know:
Applications of TensorFlow:
NumPy means “Numeric Python,” it is a fundamental package for numeric computation in Python and contains multiple n-dimensional array objects. It has more than 18K comments on GitHub and an active community of 700 contributors. It is a general-purpose library that offers multi-dimensional objects named as arrays or tools to work with them. It also resolves the speed problem with the help of multi-dimensional arrays and provides operators or functions to work efficiently with those arrays.
NumPy is considered as the most popular machine learning Python library available so far. TensorFlow utilizes NumPy to perform multiple operations on Tensors. Array interface is the most important and best feature of NumPy till now.
Features of NumPy:
Applications of NumPy:
SciPy means “Scientific Python,” it is another open-source and free library extensively used with data science or machine learning projects to perform high-level computations. The library has around 19K comments on GitHub and an active community of 600 contributors. It is good for scientific and technical computations that extends NumPy and provides many user-friendly routines for scientific computations.
Features of SciPy:
Applications of SciPy:
It is a Python library associated with NumPy and SciPy. It is highly utilized for working with complex data. There are a lot of modifications have been made to the library since the time of its inception. One of the highlighting features is cross-validation that provides the ability to use more than one metric. Training methods associated with this Python library are also improved largely.
Features of SciKit:
Applications of SciKit:
The library contains a number of algorithms for implementing data science and machine learning tasks like regression, reducing dimensionality, and model selection, etc.
Pandas mean Python data analysis library that is considered as a must-have requirement in data science. It is highly popular and used worldwide along with NumPy. It has more than 1700 comments on GitHub and 1700 contributors in an active Pandas community. It is majorly used for data cleaning and data analysis. It offers highly flexible data structures that are designed to work with structured data intuitively.
Features of Pandas:
Applications of Pandas:
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This library offers powerful visualizations and plotting library for Python. It has 26K comments on GitHub and an active community of 700 contributors. Graphs and plots designed by this Python library extensive used for data visualization. It follows the object-oriented approach to embed those plots into applications.
Features of Matplotlib:
Applications of Matplotlib:
Kera is considered one of the most amazing machine learning libraries in Python. It is an easy platform for expressing neural networks. It provides the best utilities for processing datasets, models, visualizations of graphs, etc. in the backend, it can be used as TensorFlow or Theano internally. It is a little slow when compared to other similar Python libraries. It helps in creating a computational graph with the help of a back-end infrastructure that makes it usable to perform operations. Mostly all models in Kera are Portable.
Features of Kera:
Applications of Kera:
It is the largest machine learning library that allows developers to perform tensor computations by creating dynamic computation graphs, calculate gradients automatically, and more. It offers rich application APIs to solve issues related to neural networks.
This machine learning Python library is based on the Torch that is an open source library implemented in the C programming language by Lua. It was introduced in 2017 and gained immense popularity in a short time span only.
Features of PyTorch:
Applications of PyTorch:
It is one of the best machine learning libraries that helps developers in building new algorithms with the helps of redefined elementary models, mainly decision trees. These are special libraries that are designed for fast implementation of models. There are more similar libraries that are used almost for the same purpose.
Features of LightGBM:
Applications of LightGBM:
This library provides highly scalable, optimized, and fast implementation of gradient boosting. It is common among full-stack developers because of its flexible algorithms and its benefits.
Mostly the outcome of machine learning model predictions is not accurate, but ELI5 python library helps to overcome this challenge. It debugs all machine learning models and tracks different working steps of an algorithm.
Features of ELI5
It supports other libraries and can be used to perform multiple tasks together with the help of other libraries.
Applications of ELI5
I hope these top 10 Python libraries would help you kickstart your learning on the Python libraries. If you want to learn more about Python, you can refer our Python certification program or our blogs on Python. Also, join the demo class before you enroll for the program.
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