A wide range of research projects employ Python’s robust ecosystem of libraries. Here are the top five Python libraries that are frequently used for research:
1. NumPy: The foundational Python library for scientific computing is called NumPy. It is crucial for numerical calculations, data manipulation, and linear algebra operations since it supports arrays, matrices, and a large variety of mathematical functions. Find the official documentation: https://numpy.org
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2. Panda: Pandas is a flexible library for handling and analyzing data. The Data Frame, a potent data structure that makes it easier to clean, alter, explore, and analyze data, is introduced. Working with structured data makes use of it particularly well.
Image Source: https://pynative.com/wp-content/uploads/2021/02/dataframe.png
3. Matplotlib: Python users frequently use Matplotlib to build static, animated, and interactive visualizations. It helps researchers create a variety of plots, graphs, and charts that help them properly visualize their data and communicate their findings.
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4. SciPy: On top of NumPy, SciPy adds extra computational capabilities for science and technology. It has modules for signal processing, interpolation, integration, optimization, linear algebra, statistics, and more. NumPy’s capabilities are improved by SciPy, which is a popular research tool.
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5. Scikit-learn: Various supervised and unsupervised learning techniques can be used using the resources provided by the machine learning package scikit-learn. It is frequently employed by researchers to do out tasks like dimensionality reduction, grouping, regression, and classification. It has an intuitive UI and is well-documented.
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