When talking about Python’s popularity in both the programming and Data Science community, the first thing that arrives in mind is its integrity. One of the best specialties of Python is its inherent integrity and readability that makes it a beginner-friendly language.
As every language has its own libraries and to be a good developer, it is not essential to learn all the libraries. Therefore, there are some libraries that every developer should learn and know well.
This blog is aimed at newbie developers who want to. It is not a complete reference work on Python libraries, but rather, highlights the several libraries that are available and briefly covers their purposes.
Top 7 Python Libraries that Every Developer Should Learn
oogle in 2020. It is a free, open-source python machine learning library that allows developers to work with numerical computation. It is very easy to learn and has a handful collection of helpful tools.
It is based upon data flow graphs that are used in GPUs and CPUs with a single API. However, it is not restricted to machine learning only; you can also utilize it for dataflow and programs that are differentiable.
Features of TensorFlow
TensorFlow uses automatic high-performance APIs such as — Keras.
It offers an immediate iteration of machine learning models.
This library features eager execution, which allows you to create, manipulate, and make the debugging way easier.
With TensorFlow, we can easily imagine each and every part of the graph.
With TensorFlow, you can easily move your ML models in clouds, on any device and on-premises in any browser.
TensorFlow comes with an easy to learn architecture. You can easily develop your concept into code and make your publications even easier.
It has a solution to all of your common machine learning issues. You can easily implement it and go for giving your best.
NumPy is known as one of the most popular machine learning library in Python. NumPy is generally used for scientific computation. TensorFlow and other libraries use NumPy within for performing multiple operations on Tensors. Array interface is the best and the most prominent feature of NumPy.
It enables us to operate multidimensional arrays. Arrays implementation is not there in Python so mainly the developers use NumPy in their machine learning projects. It’s easy to learn and is an open-source library that provides advanced math functionalities and a fundamental package for scientific computing with Python.
Almost every machine learning technician or data scientist applies this module for complex mathematical computations. While NumPy is a helpful Python package for a sort of general-purpose programming tasks, it’s especially great if you want to do machine learning since it provides part of the foundation for libraries like TensorFlow.
Features of NumPy
Makes coding real simple and understanding the concepts is easy.
This python package provides useful tools for integration. You can easily integrate NumPy with programming languages such as C, C++, and Fortran code.
Broadly used, therefore a lot of open source contributions.
NumPy provides such functionalities that are comparable to MATLAB. They both allow users to get faster with operations.
SciPy is a machine learning library that is used for both application developers and engineers. SciPy is one of the core packages that offer up the SciPy stack. It provides many user-friendly and effective numerical methods such as methods for numerical integration and optimization.
However, computation is not the only job that makes SciPy special. It is also very famous foell.
SciPy supplies for various scientific computing tasks that manage data optimization, data integration, and data modification. Just like NumPy, the multidimensional models are the main purposes in SciPy, which are given by the NumPy module itself.
SciPy is a useful tool for a wide sort of scientific, mathematical, and engineering tasks that lack some manipulation of numbers.
Features of SciPy
SciPy fulfills all the efficient numerical routines like optimization, numerical integration.
All the functions in all submodules of SciPy are well documented.
It makes the best use of NumPy arrays for general data structures. In fact, NumPy is an integrated part of Scipy.
Scipy can handle 1-d polynomials in two ways. Whether you can use poly1d class from NumPy or you can use co-efficient arrays to do the job.
Keras is recognized as one of the coolest machine learning libraries in Python. People who want to learn deep neural networks, Keras can be a really good option for them.
Developers who work with Keras are fascinated with its user-friendly and modular structure. It provides an easier mechanism to signify neural networks. Keras also provides some of the best services for compiling models, processing data-sets, visualization of graphs, and much more.
Keras has the ability and resources to run on top of popular deep learning libraries like TensorFlow, Theano, or CNTK. It also presents a comparably simple API that manages to also offers a lot of flexibility. This makes Keras easy to learn and simple to use.
This user-friendly, extensible tool allows easier development of deep learning models. It is a great, effective tool for beginners, too, and can run seamlessly on CPUs and GPUs.
Features of Keras
Keras, being modular in nature is amazingly expressive, flexible, and well-suited for innovative research.
It doesn’t only support neural networks only but also provides a fully supportive environment for convolutional and recurrent neural networks.
It runs smoothly on both CPU and GPU.
This python library features a variety of implementations from neural networks forming blocks — functions, layers, optimizers, objectives, and others.
Keras also features many useful tools that allow you to work with different images and texts easily.
Using Keras, you can build deep models for smartphones — both Android and iOS or for Java Virtual Machine also.
Pandas is a machine learning library in Python that gives data structures of high-level and a broad variety of tools for analysis. One of the great features of this library is the capability to translate complex operations with data using one or two commands.
It is a must to learn for data-science and dedicatedly formulated for Python language. It is a fast, definite, and flexible platform that allows intuitive data-structures.
As Pandas is a data analysis module, we can refine the data most efficiently using the pandas’ library. It allows different kinds of data structures that are able to work.
Features of Pandas
Pandas make sure that the entire process of manipulating data will be easier.
Smart alignment and indexing featured in Pandas offer you a perfect organization and data labeling.
Pandas have some special features that allow you to handle missing data or value with a proper measure.
This package offers you such a clean code that even people with no or basic knowledge of programming can easily work with it.
It provides a collection of built-in tools that allows you to both read and write data in different web services, data-structure, and databases as well.
Pandas can support JSON, Excel, CSV, HDF5, and many other formats. In fact, you can merge different databases at a time with Pandas.
Theano is a computational framework machine learning library in Python and it is a popular choice for performing neural network models. Theano works similarly to TensorFlow, but it is not as effective as TensorFlow.
It can analyze, define, optimize, and modify different mathematical information at the same time. As Theano gets the best use of multi-dimensional arrays, you hardly have to worry about the completion of your projects.
Moreover, Theano can also be applied in distributed or parallel environments just similar to TensorFlow.
Features of Theano
Ability to use NumPy arrays effectively in Theano-compiled functions.
Perform data-intensive computations much quicker than on a CPU.
Theano performs your derivatives for functions with one or many inputs.
Evaluate expressions faster than ever before, thereby, increasing efficiency by a lot.
Detect and diagnose multiple types of errors and ambiguities in the model.
Theano allows you to avoid dirty bugs while working with expressions. You can work seamlessly on expressions without wasting any time.
This library makes computation 140x faster. Computation of data-intensive applications is easier with Theano.
PyTorch is the biggest machine learning library and a popular open-source framework for machine learning and deep learning. This machine learning library is based on Torch, which is an open-source machine library performed in C with a wrapper in Lua.
It’s a useful tool for developing and training neural networks. It optimizes tensors for deep learning using CPUs and GPUs, and it also accelerates the way from research to prototyping to production deployment.
Other than this, PyTorch offers rich APIs for resolving application issues related to neural networks. This is the most used machine learning library, assisting planners to perform tensor computations with the acceleration of a GPU.
Features of PyTorch –
PyTorch can be used with other popular libraries, as well. You can easily integrate it with libraries/packages like Cython and Numba.
PyTorch uses TorchScript, which offers a flexible and simple eager mode. You can evaluate different functions and operations instantly.
While in the graph mode, PyTorch provides absolute transitioning, fast optimizations, and offers a C++ run-time environment.
PyTorch has good support for async. Execution for cumulative operations. This way, you can boost up your project performance.
This library also allows P2P (Peer to Peer) communication, which can be gained by both Python and C++.
Python Packages and Libraries play an important role in a developer’s profession. Whether it is for data science or machine learning or any other viewpoints of the programming world, these libraries are all here to meet you up.
We hope this blog encouraged you to kick start your learning on the libraries available in Python. We guess you got to know about different modules, libraries, and frameworks for Python.