😀 Python Excellent top "11" famous library uses in Data Science, Artificial intelligence, Machine Learning, Deep Learning
Data Analysis and...many more
There are "11" Excellent library to help you make the new concepts, you are learning and to become a "Data Scientist" .it's really very important.
There are:
Top 11 Python Libraries
1.Numpy
2.Pandas
3.Matplotlib
4.Scikit-learn
5.Keras
6.PyOD
7.PyTorch
8.Psycopg
9.TensorFlow
10.OpenCV Python
11.Selenium Python
1.Numpy-It is a very important library on which almost every data science or machine learning NumPy is very useful for performing mathematical and logical operations on Arrays. It provides an abundance of useful features for operations on n-arrays and matrices in Python. This course covers basics things to know about NumPy as a beginner in Data science. These include how to create NumPy arrays, use broadcasting, access values, and manipulate arrays. More importantly, you will learn NumPy’s benefit over Python lists, which include: being more compact, faster access in reading and writing items. being more convenient and more efficient.
Advantages-
1.NumPy uses much less memory to store data
2.Using NumPy for creating n-dimension arrays
3.Mathematical operations on NumPy n-Dimension Arrays
4.Finding Elements in NumPy array
2.Pandas-They provide you with a huge set of important commands and features which are
used to easily analyze your data. We can use Pandas to perform various tasks
like filtering your data according to certain conditions, or segmenting and
segregating the data according to preference, etc.
Advantages-
1.Less writing and more work done.
2.Efficiently handles large data
3.Makes data flexible and customizable
4.Data representation
3.Matplotlib-It is a python library used for Data Visualization. You can create bar-plots, scatter-plots, histograms, and a lot more with matplotlib. Data Visualization is an essential component of a Data Scientist’s skill set. It is extremely necessary to show the insights found from the analysis of the data in the form of beautiful, aesthetic graphs and this is where matplotlib comes to our rescue.
Advantages-
1.Basemap: It is a map plotting toolkit with various map projections, coastlines, and political boundaries
2.Cartopy: It is a mapping library featuring object-oriented map projection definitions, and arbitrary point,
line, polygon, and image transformation capabilities.
3.Excel tools: Matplotlib provides utilities for exchanging data with Microsoft Excel
4.Mplot3d: It is used for 3-D plots.
4.Scikit-learn-it is probably the most useful library for machine learning in Python. The Scikit-learn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction. Please note that sklScikit-learn earn is used to build machine learning models.
It should not be used for reading the data, manipulating, and summarizing it. There are better libraries for that (e.g. NumPy, Pandas, etc.)
Advantages-
1. This library is distributed under the BSD license
2. sci-kit-learn library is very versatile
3. This library serves real-world purposes like the prediction of consumer behavior
4.This library creation of neuroimages
5.Keras -It'scontains numerous implementations of commonly used neural-network
building blocks such as layers, objectives, activation functions, optimizers, and a
a host of tools to make working with image and text data easier to simplify the
coding necessary for writing deep neural network code. The code is hosted on
GitHub, and community support forums include the GitHub issues page and an SLA
Keras is an open-source library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library. Up until version 2.3, Keras supported multiple backends, including TensorFlow, Microsoft Cognitive Toolkit, R, Theano..and many more
Advantages-
1.Very simple to use
2. broad adoption, support for a wide range of production deployment options
integration with at least five back-end engines
3.strong support for multiple GPUs and distributed training
4.it is used for backend Support
6.PyOD-It is an excellent Python Outlier Detection (PyOD) library.
It efficiently works on an extensive multivariate data set to detect anomalies.
It supports many outlier detection algorithms (approx. 20), both standard and some quite recent neural network-based ones. Also, it has a well-documented and a unified API interface to write a cleaner and robust code.
PyOD library helps you execute the three main steps for anomaly detection
Advantages-
1.Build a model
2.Define a logical boundary
3.Display the summary of the standard and abnormal data points
Please note that the PyOD library is compatible with both Python2 and Python3 and that too across major operating systems.
7.PyTorch-It is an open-source Python library and works on top of the Torch library.
This library offers two high-level features. Tensor computing with high acceleration utilizing graphics processing units (GPU) Deep neural networks (Using a tape-based auto diff system PyTorch developer provisioned this library to run numerical operations quickly. And the Python programming language complements this methodology. It makes machine learning engineers run, debug, and test part of the code in real-time. Therefore, they can identify any problem even when the execution is in progress.
Some of the critical highlights of PyTorch are:
Advantages-
1.Simple Interface –The API set is quite easy to integrate into Python programming
2.Pythonic Style –It smoothly gels into the Python data science stack. Therefore
all the services and features are accessible by default
3.Computational Graphics–PyTorch gives a platform to generate dynamic
4.computational charts. It means you can update them while running
8.Psycopg-It is the most reliable database management system. It is free, open-source, and robust. If you wish to use it as the backend for your data science project, then you need Psycopg Psycopg is a database adaptor for PostgreSQL written in Python programming language. This library provides functions confirming to Python DB API 2.0 specifications.
Advantages-
1.This library has native support for heavily multi-threaded applications.
2.Require concurrent INSERTs or UPDATEs and closing a lot of cursors.
9.TensorFlow-It is used to create Deep Learning models and machine learning apps like neural networks. Initially, its development began at Google, and later it was open for public contribution.
TensorFlow gives you the ability to design machine learning algorithms, whereas
sci-kit-learn provides out-of-the-box algorithms such as SVMs, Logistic
Regression (LR), Random Forests (RF), etc.
It is undoubtedly the best deep learning framework. Giants like Airbus, IBM, Twitter
, and others are using it due to its highly customized architecture.
While TensorFlow produces a static graph, PyTorch provides dynamic plotting.
TensorFlow comes with TensorBoard, an excellent tool for visualizing ML models whereas PyTorch doesn’t have any.
Advantages-
1.Its use in Data visualization
2.Keras friendly
3.Scalable
4.Compatible
5.Parallelism
6.Architectural support
10.OpenCV Python-
It uses NumPy under the hood. Finally, all OpenCV-Python types convert to the NumPy data structure.OpenCV is a reliable name in the field of image processing. And OpenCV-Python is the Python library that provides functions for parsing an image.
Advantages-
1.First and foremost, OpenCV is available free of cost
2.Since the OpenCV library is written in C/C++ it is quite fast
3.Low RAM usage (approx 60–70 MB)
4.It is portable as OpenCV can run on any device that can run C
11.Selenium Python-It is one of the coolest tools for web automation testing. However, it is quite rich in functionality, and one can easily use its APIs to create web crawlers. We have provided in-depth tutorials to learn to use Selenium Python.
Selenium is a tool to test your web application.
You can do this in various ways, for instance
Advantages-
1.More realistic browser interaction.
2.A separate component such as the RC server is inessential.
3.Faster Execution time.
4.Open Source
5.Capability to run tests across different browsers
Conclusion-In this tutorial you will have to learn Python Excellent top "11" famous library uses and Advantages
So I hope you liked these tutorials. If you have any questions or suggestions related to Python please comment below and let us know.
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