Python Libraries for Data Science

• Extensive first and third party libraries. Top Python Libraries for Data Science.
  • NumPy (aka Numerical Python) is the core numeric and scientific computation library in Python. General-purpose array-processing package.
  • SciPy (aka Scientific Python) is extensively used for scientific and technical computations (extends NumPy).
  • Matplotlib is an essential library in Python for data visualization in data science. A plotting library.
  • Seaborn is another library in Python for data visualization. Extension of Matplotlib. Statistical and graphical analysis in data science.
  • Pandas (Python data analysis) is a foundational Python library for data analysis in data science. Data cleaning, data handling, manipulation, and modeling.
• Top Python Libraries for Data Science.
  SciKit-Learn is a robust machine learning library in Python. Data mining, feature engineering, training and deploying machine learning models. Statsmodels - provides functionalities for descriptive and inferential statistics for statistical models. TensorFlow - a framework for defining and running computations that involve tensors. Machine learning and deep learning framework. Keras is a neural network Python library for deep learning model development, training, and deployment. PyTorch - scientific computing package that uses the power of graphics processing units. Scrapy - for web crawling frameworks. BeautifulSoup - for web crawling and data scraping. NLTK (Natural Language Tool Kit) is a Python package essentially for natural language processing.