Python is a very popular language which holds multiple libraries. Python libraries are used for various purposes and help Python developers to develop impactful web applications within less time.
Do You Know?
44.1% of developers use Python and is one of the most used programming languages among developers.
According to Statista, 29.9% of people make searches related to Python and are one of the most searched programming languages in Google.
Well, by viewing the mentioned stats, you must have a question in your mind that:
Why is Python so popular?
Python Holds Supportive Community: Python was initially released in 1990, that’s why its community support is stronger, and it holds experienced developers that can clear the doubt of other developers facing the issue related to Python libraries.
Python Support Big Data: It is one of the most famous languages used in data science; additionally, it is used for machine learning, AI systems, and several other modern technologies.
Rich sets of libraries: Libraries can help you by saving development time. Python has a fantastic collection of libraries that can help you to deliver the project on time.
Easy To Learn: For a technical person, it is simple to learn Python. It’s simplified syntax, and natural language makes the learning process simple.
High Scalability: This technology allows you to handle the different sizes of tasks. It supports various programming paradigms like functional and object-centered paradigms. Python allows developers to make use of code functions rapidly, and it also allows you to frame scripts on a large scale.
Simple To Analyze: Python is highly readable and similar to the normal English language. The writing process of the language is also easy, and that’s why it is popular among the developers. The debugging processes and code reviews allow developers to resolve the issue in less time frame.
Several libraries are available on Python, but among all, few are the best, which are usually used for software, web app development. Here I am going to discuss top Python Libraries that you should know if you are planning to develop leading software for your startup, SME, or big enterprises.
10 Popular Python Libraries you must know in 2021-22
Let’s view the famous Python Libraries that are used to develop impactful software, web, and mobile applications. The Python developers generally use the below-mentioned libraries to deliver the leading projects.
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This Python library works as a computational library for writing new algorithms, and it also involves several tensor operations. Google created a TensorFlow library in collaboration with Brain Team. In this library, neural networks can be easily expressed as computational graphs, implemented by utilizing TensorFlow.
Top Features of TensorFlow
- It is adjustable in its operability.
- It is easily trainable on CPU and GPU.
- It can train multiple neural networks and GPUs.
Famous Apps Using TensorFlow
- Google Voice Search
- Google Photos
This is one of the best Python libraries as it efficiently deals with complex data. Scikit-Learn is allied with NumPy and SciPy, allow you to use more than one metric. In this library, a few improvements in logistics regression and nearest neighbors are required.
- Supports multiple methods to examine the efficiency of supervised models on unseen data.
- Contain the large spread of algorithms ( starting from clustering, factor analysis, principal component analysis to unsupervised neural networks).
- Beneficial for extracting features from images and text.
Famous Apps Using Scikit-Learn
Numpy is acknowledged as one of the most famous machine learning libraries in Python. For performing several operations on Tensors, TensorFlow and other libraries utilize Numpy internally. Array interface is the excellent and the most vital feature of Numpy.
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Features Of Numpy
- It is easy to use.
- It includes random number generators
- Makes coding simple
Keras is one of the coolest Python libraries. It proffers a simple mechanism to represent neural networks. This library provides a few best services for visualization of graphs, compiling models, processing data-sets, and so on. Few most famous neural networks like CNTK can also be used in Keras. In comparison with other Python libraries, it works slowly.
Features Of Keras
- It works evenly on both CPU and GPU.
- It almost supports all models of a neural network.
- It is incredibly expressive, flexible, and apt for ingenious research.
Famous Apps Using Keras
PyTorch offers vibrant APIs for resolving application problems related to neural networks. This Python library permits developers to implement tensor computations with an acceleration of GPU, creates dynamic computational graphs, and calculate gradients automatically.
Features Of PyTorch
- Offers flexibility in eager mode
- By taking the help of native support, it optimizes performance in both research and production.
- It can be used with other libraries and packages such as Cython and Numba.
Famous Companies Using PyTorch
- Samsung Electronics
The requests library is in practice measure for making HTTP requests in Python. It removes the complexities of building requests and offers simple API’s so that you can concentrate on application communicating with services and consuming data.
Features of Requests Library
- Customize requests by adjusting headers, authentication, query strings, etc.
- Inspect the data sent to a server
- Make requests utilizing various HTTP methods like GET, POST, and PUT.
- Its automatic decompression feature allows you to recover and revive compressed data into its factual form within no time.
Gradient Boosting is the best and most famous machine learning library, which encourages developers to build new algorithms by utilizing redefined elementary standards, namely decision trees.
The special libraries that are designed for quick and effective implementation of this method are LightGBM, XGBoost, and CatBoost. These libraries are opponents that aid in determining a common problem and can be employed in almost a similar way.
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Features of LightGBM
- Very active computation guarantees high production yield.
- Automatic and user friendly.
- Not produces an error when you consider NaN or other canonical values.
Theano is a computational framework Python library used for measuring multidimensional arrays. Theano works the same as TensorFlow did, but it is not as profitable as TensorFlow. This library can also be utilized in shared or parallel environments.
Features Of Theano
- It can completely use NumPy arrays in Theano-compiled functions
- It can perform data-intensive computations quicker than on a CPU
- Recognize and diagnose various types of errors and uncertainties
Famous Companies Using Theno
This is one of the well known Python libraries that present data structures of high-level and quality tools for critique. It can resolve complex operations with data utilizing one or two commands. The library has multiple functionalities and inbuilt systems for grouping, combining data, and filtering.
Features Of Pandas
- It offers a simple process of manipulating data.
- Support for multiple operations such as Iteration, Re-indexing, Aggregations, Sorting, Visualizations, and Concatenations.
Popular Applications Using Pandas
It is the top open-source python library used for both systematic and technical computation. SciPy includes modules for linear algebra, optimization, integration, addition, specific functions, FFT, signal and image processing, ODE solvers, and other tasks.
Features Of Scipy
- Makes the best use of Numpy arrays for producing general data structures.
- Scipy can manage 1-d polynomials in two systems that are poly1d class from Numpy, or co-efficient arrays to perform the job.
- It also supports numpy.lib.scimath.
Now, from the above top Python libraries listing, you can choose any one of the libraries to build the excellent software and application.
The FastAPI framework is a modern, fast (high-performance) and easy to use web API builder for building production ready APIs using Python with best practices. The framework optimizes developer experience so that they can write simple code by default without any headache or hassle in their way.
Features Of FastAPI
High performance if compared to NodeJS and Go
Easy to learn and 40% less human errors
Fast to code to develop features about 200% to 300%
Famous Companies Using FastAPI
People prefer the Python programming language for Application development, and that’s why the demand for python developers is rising. 182,532 live websites are using Python, and additionally, 637,816 sites historically used Python.
In order to build full-fledged Python-related web and mobile applications, it is necessary to choose one of the best Python libraries. Here the task does not end as only choosing the library can’t offer you a leading application; for creating it, you need to select the best Custom Software development Company to hire python developer.