Several Python libraries are commonly downloaded and used for the processing, visualization, and analysis of data. These include:

- **scikit-learn**: A machine learning library.
- **NumPy**: A library for scientific computing.
- **pandas**: A library for data manipulation and analysis.
- **Matplotlib**: A plotting library.

University of Michigan - Ann Arbor

Google

Python is a powerful language that allows data scientists to perform data manipulation, analysis, and visualizations through its vast number of libraries such as numpy and pandas.

Data Science with Python

Python's data science libraries can be broken down into three different categories:
- Data Mining
- Data Processing and Modeling
- Data Visualization

Categorizing Python Libraries for DS

Core Python Libraries for Data Science

- **NumPy**: Used for array operations and scientific/data computing.
- **Pandas**: Used for data manipulation, cleaning, and analysis via dataframes.
- **Matplotlib**: A core plotting library offering multiple data visualization options.
- **Seaborn**: Built on Matplotlib, used for creating attractive statistical graphics like heatmaps.
- **BeautifulSoup**: Used for web crawling and scraping data from HTML/XML files.
- **SciPy**: An open-source scientific computing library covering statistics, integration, optimization, and image processing.
- **StatsModels**: Provides classes and functions for estimating and testing various statistical models.

Learn Before

Related