8 Best Python Data Manipulation and Analysis Libraries

Here are the best open-source Python data manipulation and analysis libraries for your applications. You can use these for easy manipulation, cleaning, and preparation of datasets directly in your apps.

These Python libraries provide fast and flexible data structures and analysis capabilities, such as DataFrame and series. They were empowering users with features like indexing and merging of data. Also, you can find libraries in Python for efficient array computations, offering a wide range of mathematical and statistical functions focusing on operations on arrays. Certain libraries also provide advanced functions for scientific computing, such as optimization, signal processing, and statistics. They are built to work together and commonly perform various data manipulation and analysis tasks. Some libraries are specially meant for data visualization. They allow developers to plot and visualize large datasets clearly and meaningfully.

We have handpicked top and trending open-source Python data manipulation and analysis libraries for your next project. The below-mentioned libraries are widely used in the data science community and have extensive documentation and tutorials available.  

NumPy:

• Used in Utilities, Data Manipulation, Numpy applications, etc.
• It’s a fundamental package for scientific computing in Python.
• Provides a powerful N-dimensional array object.
• Offers a range of tools for array manipulation.

Pandas:

• Used for data manipulation and analysis in Python.
• Offers a range of functions for data manipulation.
• Provides fast, flexible, and expressive data structures.
• Features include data filtering, aggregation, and transformation.

Matplotlib:

• Used for creating static, animated, and interactive visualizations in Python.
• Provides a range of tools for creating charts and graphs in Python.
• Offers data visualization and plotting, including 2D and 3D plotting and animation capabilities.
• Works with Python scripts, Python/IPython shells, web application servers, and several graphical user interface toolkits.

Seaborn:

• Used in Analytics, Data Visualization, Pandas applications, etc.
• Provides a range of tools for creating statistical graphics in Python.
• Allows visualizing statistical models and distributions.
• Built on top of Matplotlib.

SciPy:

• Used for scientific computing, including algorithms for optimization, signal processing, linear algebra, and more.
• Built to work with NumPy arrays.
• Functions include optimization, integration, interpolation, and more.

scikit-learn:

• Used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Pandas applications, etc. 
• Offers various algorithms for classification, regression, clustering, and more.
• Provides a range of tools for classification, regression, and clustering.
• Built on top of SciPy.

Statsmodels:

• Used for statistical modeling and hypothesis testing.
• Offers linear and non-linear regression and time series analysis capabilities.
• Features include regression analysis, time-series analysis, and more.
• Provides a complement to SciPy for statistical computations.

PyTorch:

Used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Numpy applications, etc.

Provides various tools for creating and training deep learning models.

It can be extended using Python packages such as NumPy, SciPy, and Cython.