Python is currently the most popular programming language. Majority of the data scientists use python programming on a daily basis. There are various advantages of python, which includes ease of learning and debug, object-oriented, open-source. Python is designed with various Python libraries for data science used by programmers for problem solving, top libraries are mentioned below.
List of Top Python Libraries for Data Science
There are many Python Libraries for Data Science which are used for different purposes. They include different types of applications as well as features. The top python libraries are mentioned below:
Matplotlib
Matplotlib is a library which is used for creating animation, static, and interactive visualizations. Its MATLAB-like interface and extensive support for plots and charts make it perfect for data visualization and exploration.
Pandas
Pandas is a most used and powerful library used for data analysis and data manipulation.It is necessary for any data science project since it makes tasks like data cleaning, transformation, and exploration easier.
NumPy
The core library for scientific computing in Python is called NumPy. Numerous mathematical procedures can be carried out on large, multi-dimensional arrays and matrices. In the data science ecosystem, NumPy serves as the foundation for numerous other Python Libraries for Data Science.
Scikit-learn
Scikit-learn is a versatile machine learning library which provides an effective and simple tool for data mining and analysis. It is incredibly useful for developing and implementing machine learning models because it has multiple algorithms for classification, regression, clustering, dimensionality reduction, and model selection.
SciPy
NumPy is the foundation for SciPy, a library of mathematical functions and algorithms. It is a crucial addition to NumPy for work involving scientific computing because it offers tools for linear algebra, optimization, integration, interpolation, and more.
TensorFlow
Google developed the machine learning framework TensorFlow, which is available for free. It provides a comprehensive ecosystem of tools, libraries, and community resources for building and deploying machine learning models, including deep learning algorithms.
Seaborn
Based on Matplotlib, Seaborn is a data visualization library that provides a high-level interface for making visually appealing and educational statistical visuals. It simplifies the process of visualizing complex datasets and facilitates the exploration of relationships between variables.
Keras
Keras is a high-level neural networks API written in Python, capable of running on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit (CNTK). It gives quick access to deep neural network experimentation and offers an intuitive model construction and training interface.
Plotly
Plotly is a versatile graphing library that supports interactive, publication-quality plots. It offers a range of APIs for creating graphs in Python, R, and JavaScript, making it suitable for a wide range of applications, from exploratory data analysis to dashboard creation.
PyTorch
Facebook developed PyTorch, an open-source machine learning framework. It provides a flexible and dynamic computational graph system that facilitates the development and training of deep learning models, particularly in research settings.
Beautiful Soup
A Python package called Beautiful Soup is used to parse HTML and XML documents, extract data, and manipulate the parse tree. It is commonly used for web scraping tasks, allowing data scientists to gather information from websites and incorporate it into their analyses.
Scrapy
Scrapy is a powerful and flexible web crawling framework for Python, used to extract data from websites and APIs. It provides a robust architecture for building web spiders and pipelines, making it ideal for large-scale data extraction tasks.
Statsmodels
Statsmodels is a Python library for estimating and interpreting statistical models. It offers a wide range of regression models, time series analysis tools, and hypothesis tests, making it suitable for statistical analysis and modeling in various domains.
LightGBM
Tree-based learning methods are used in the gradient boosting framework LightGBM. It is designed for efficiency and scalability, making it well-suited for large datasets and high-dimensional feature spaces. LightGBM is widely used for classification, regression, and ranking tasks.
OpenCV
OpenCV (Open Source Computer Vision Library) is a comprehensive library for computer vision tasks. It provides a wide range of functionalities for image and video processing, including object detection, feature extraction, image stitching, and more.
Theano
With the help of the Python numerical computation package Theano, multi-dimensional array-related mathematical expressions can be efficiently defined, optimized, and evaluated. It is particularly well-suited for deep learning research and development.
XGBoost
XGBoost is an optimized gradient boosting library that offers high performance and scalability. It is widely used for supervised learning tasks, such as classification and regression, and has won numerous machine learning competitions due to its accuracy and efficiency.
Altair
Based on the Vega and Vega-Lite visualization grammars, Altair is a declarative statistical visualization toolkit for Python. It allows for the creation of interactive visualizations with concise and intuitive syntax, making it accessible to both beginners and experienced users.
Ggplot2
Ggplot2 is a data visualization library for R, inspired by the Grammar of Graphics. It provides a powerful and flexible system for creating graphics, allowing users to specify plots in terms of data, aesthetics, and layers.
Bokeh
To create interactive visualizations in web browsers bokeh is the best python library. It offers a versatile and flexible API for building interactive plots, dashboards, and applications, with support for streaming and real-time data.
CatBoost
CatBoost is a gradient boosting library designed with categorical features in mind. It performs better than existing gradient boosting libraries and manages categorical variables automatically, which makes it perfect for datasets including a variety of data types.
ELI5
ELI5 is a Python library which is used to evaluate and debug machine learning models. In order to help users understand and develop confidence in their models, it offers tools for visualizing model internals such as features importance and decision paths.
NuPIC
NuPIC is Numenta Platform for Intelligent Computing. It is a library used for machine learning for online prediction and anomaly detection which is based on the principles of hierarchical temporal memory (HTM).
Ramp
For automating model selection as well as evaluation and building machine learning pipelines, Ramp is the most used and preferred tool. It has a user-friendly interface for experimenting with various preprocessing techniques as well as algorithms for facilitating rapid prototyping and development.
Pipenv
For managing python virtual environments and dependencies, Pipenv is the best library or a tool. It streamlines the process of managing project dependencies and establishing repeatable environments by integrating the features of virtualenv and pip into a single workflow.
Frequently Asked Questions
Which is the best institute to learn python?
If you are searching for the best institute, then we will recommend Milestone Institute of Technology where you can get quality training with live projects, career guidance from experts with certifications and placements.
Which Python library is best for data visualization?
There are many different libraries used for data visualization, but when it comes to choosing best then Matplotlib, Seaborn, Plotly, and Bokeh are the libraries which we will recommend and mostly use. They have unique qualities and functionality.
What is the use of python libraries for data science?
Python libraries include pre-built functions as well as tools which are used for data manipulation, statistical modeling, and visualization for simplifying the analysis processes. They actually improve productivity by making tasks more simple.
Which are the commonly used Python libraries for data science?
There are many different popular python libraries like NumPy, panda, Matplotlib, and scikit-learn, other popular python libraries used in data science are NLTK or spaCy for tasks involving natural language processing, TensorFlow and Python for deep learning, and seaborn for statistical visualization. These libraries serve a variety of analytical purposes.