Python is the top used programming language in data industry.
So on this article we are going to be talking about the top Python libraries used in data science. When it comes to problems solving Python is the best program language to leverage on.
Know about about their individual features, areas of application, and how they can help with numerical computing, data visualization, machine learning and more.
Top Python Library used by Data science
Here are the top Python libraries used in data science
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NumPy
numerical computing library with support for arrays, matrices, and high-level mathematical functions.
Features
support for arrays, matrices, and high-level mathematical functions.
Areas of application
- numerical computing
- linear algebra
- Array processing
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Seaborn
library based on Matplotlib that provides a high-level interface for producing statistical graphics.
Features
- high-level interface for producing statistical graphics.
Areas of application
- Data visualization and statistical plotting
- Producing visualizations for exploration and presentation.
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Scikit-learn
This is a Python library that gives simple and efficient tools for data mining and data analysis.
Features
- simple and efficient tools for machine learning
Areas of application
- Machine learning, data mining, and data analysis.
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TensorFlow
open-source Python library specialized mainly for machine learning and deep learning.
Features
- High-level neural networks API.
Areas of application
- It is use in Deep learning, artificial neural and machine learning.
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SciPy
library for scientific computing in Python that provides functionality for working with arrays, numerical optimization, signal processing, and more.
Features
- library for scientific computing in Python that provides functionality for working with arrays, numerical optimization, signal processing, and more.
Areas of application
- scientific computing, numerical optimization, and signal processing.
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Statsmodels
This library is used for statistical modeling, hypothesis testing and data exploration. It provides tools for regression analysis, time series analysis, and other statistical methods.
Features
- library for statistical modeling, hypothesis testing, and data exploration.
Areas of application
- statistical modeling, hypothesis testing, and data exploration.