12.1: Python for Data Analytics

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Page ID: 48134

Felix Amoruwa
De Anza College

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Python for Data Analytics

Python is a versatile, general-purpose language widely used in data analytics. Its syntax is easy to learn, and it has a large ecosystem of libraries for data manipulation, scientific computing, and machine learning. The core libraries include:

NumPy: Fundamental package for numerical computing with array objects. It provides fast operations on arrays and matrices and underlies many other libraries [3].
pandas: Provides DataFrame structures similar to R data frames. Pandas includes tools for loading, cleaning, and transforming data (e.g. read_csv, groupby, merge). Its development was described by McKinney (2010) as enabling “data structures for statistical computing in Python”[3].
scikit-learn: A machine learning library with tools for classification, regression, clustering, and preprocessing. The library’s API emphasizes ease of use and consistent interface. It was introduced in the Journal of Machine Learning Research by Pedregosa et al. (2011) [5].

Figure \(\PageIndex{1}\): Python Data Analytics Ecosystem. A layered diagram showing the relationship between Python's core analytics libraries -- NumPy as the foundation, pandas built on top for data manipulation, and scikit-learn at the application layer for machine learning. (CC BY 4.0; Felix Amoruwa) Access to a detailed description of Figure \(\PageIndex{1}\)

These libraries build on general Python features and are typically used within interactive or scripted environments.

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