Python libraries for data science

 Python libraries for data science

Python is more prevalent among data scientists due to its embedded characteristics. Although a dozen programming languages are available for data analysis, data mining, and machine learning, Python possesses a special place in the field which ranked as the number 1 programming language in the TIOBE index for data scientists in 2023. 

Python is a high-level, interpreted programming language known for its simplicity, readability, and versatility. It was created by Guido van Rossum and released in 1991. 

Key Features of Python:

1. Readability: Python's syntax emphasises code readability and uses indentation to define code blocks rather than relying on braces or keywords.

2. Easy to Learn: Python's codes are described as "executable pseudocode" due to their resemblance to natural language. 

3. Versatility: Python can be used in many applications, including machine learning, artificial intelligence, web development, scientific computing, and data analysis. 

4. Comprehensive standard library: Python has a vast number of standard libraries that provides a wide range of modules and packages for various everyday tasks such as file I/O, string manipulation, data serialisation, networking and more. 

5. Cross-Platform Compatibility: Python developers can code in one platform and run it on different platforms without significant modifications. 

6. Extensive Ecosystem: Python consists of third-party libraries and frameworks. These libraries can complete a handful of tasks, including data analysis, machine learning, numerical computing, scientific computing, and web development. This article is dedicated to discussing more of these external/third-party Python libraries. 



Data scientists spend their time on tasks such as data cleaning, exploratory data analysis, statistical modelling, machine learning, data visualisation and more. Python has a comprehensive set of third-party libraries for performing the everyday tasks of data scientists. This article introduces the most popular and essential set of libraries as follows.

Pandas 

Pandas is a famous data manipulation and analysis library in Python. Pandas provides high-performance data structures such as Data frames and series. Additionally, it offers a wide range of functions, including data manipulation, cleaning, exploration and analysis. Here are some key features and functionalities of it. 

DataFrame: Data frames are the fundamental data structure in pandas, similar to a data table with columns and rows. These data structures are easy to manipulate and analyse.

Data manipulation: Data scientists utilise Pandas for efficient data preparation and transformation. Pandas enable the functions such as filtering values, sorting data, reshaping data and more. 

Data cleaning: Data scientists spend most of their time with data preparation and cleaning. Pandas offer many functionalities for them to perform the tasks such as handling missing values, outliers, inconsistent formatting, data imputation, and more.   

Data exploration: Pandas offers functions for descriptive statistics, data summarisation, and aggregation, which allows users to calculate various statistical measures, generate frequency tables and visualise data dispersion. 

The syntax for importing Pandas :



NumPy 

Numpy stands for "Numerical Python", devoted to numerical computing. As the name implies, it provides a range of mathematical functions and supports for large multi-dimensional arrays and matrices to perform numerical computations efficiently. Some of the key features and functionalities of the NumPy library are as follows : 

Ndarray: The n-dimensional array (Ndarray) is the primary data structure in NumPy, a fixed-size, homogeneous array with an arbitrary number of dimensions. Ndarrays can be used in fast and efficient operations on massive datasets. 

Indexing and slicing: This is useful in extracting subsets of arrays or modifying specific array elements. NumPy provides capabilities to access and manipulate array elements by indexing and slicing.

Random number generation: Random numbers and random arrays can be generated using the functions of the NumPy library. 

Syntax for importing Numpy :




Matplotlib 

Matplotlib is a popular data visualisation library in Python. It comprises a comprehensive set of functionalities for generating a wide range of plots, charts, and visualisations. Here are some key features and functionalities of Matplotlib. 

Plotting functions: Line plots, bar plots, scatter plots, area charts, histograms, pie charts, violin plots, box plots and many more types of visualisations can perform with the functions of the Matplotlib library, which provides a comprehensive level of customisation to create appealing plots. 

Multiple output formats: Matplotlib allows you to save the plots in different formats, such as PNG, JPEG, PDF, SVG, and more.

Subplots and Layouts: Matplotlib offers to create multiple subplots within a single figure, which can display in a grid-like layout. This feature can make a comparison of different datasets or visualisation of various aspects of a dataset. 

Syntax for importing Matplotlib :



Seaborn 

Seaborn specialises in generating attractive and informative statistical graphics. Some key features of the Seaborn are as follows: 

Improved Aesthetics: Seaborn consist of pleasant colour palettes and predefined themes to enhance the aesthetics of generated visualisations. 

Multi-plot grids: Seaborn offers to generate multi-plot grids for exploring complex relationships within a dataset. The functions in Seaborn, such as "FacetGrid" and "PairGrid", allow the creation of subplot grids. 

Time series visualisation: Seaborn provides functionality for visualising time series data. The temporal patterns, trends, and seasonality in time series data can be captured using the functions of Seaborn.

Syntax for importing Seaborn :





Scikit-Learn 

Scikit-Learn is a popular machine-learning library in Python. It offers machine-learning algorithms and toolsets for data preprocessing, feature extraction, model selection, and evaluation. Some key features and functionalities of the Scikit-Learn are as follows:

Consistent API: Scikit-Learn provides a uniform interface for all its algorithms, with consistent methods for fitting, predicting, and evaluating models. The consistency makes it easy to use and learn. 

Machine learning algorithms: Scikit-Learn provides many supervised and unsupervised machine learning algorithms. It consists of algorithms for regression, classification, clustering, dimensionality reduction, and more. 

Pipelines: Pipelines are a powerful tool for streamlining the machine learning workflow by combining the multiple data processing steps and machine learning models into a single chained object. Scikit-Learn supports building pipelines to simplify the process of training and deploying machine learning models. 

Syntax for importing Scikit-Learn :



SciPy 

Scipy is shortened from "Scientific Python", which offers a wide range of scientific and numerical computing capabilities. SciPy builds upon the NumPy library with an extension of additional tools and algorithms. The collection of functionalities and algorithms of the SciPy library is popular in various domains such as mathematics, physics, engineering, biology and more. Here are some key features and functionalities: 

Numerical integration and optimisation: SciPy provides functions for numerical integration: quadrature, differential equations, and optimisation algorithms: finding the minimum and maximum of a function. 

Signal and Image Processing: The signal processing module of SciPy consists of functionalities for filtering, Fourier analysis, wavelet transformation and many more. The image processing functionalities such as segmentation, feature extraction, and image manipulation are also available in the Scipy library. 

Statistical Analysis: SciPy provides a comprehensive set of statistical functionalities for descriptive statistics, hypothesis testing, regression analysis, ANOVA etc. Moreover, random number generation from different probability distributions, calculating probability density function, cumulative distribution function, and quantiles are also available in the SciPy library. 

Syntax for importing SciPy :




Overall, Python libraries play a crucial role in enhancing the capabilities and efficiency of the Python language. The rich ecosystem of tools and resources of Python libraries provides a wide range of functionalities for data scientists to tackle various tasks and challenges. Further, Python libraries empower developers to solve complex issues across multiple domains. Whether you're working on data analysis, machine learning, web development, or scientific computing, Python libraries offer a wide range of functionalities, making Python an attractive choice for developers and data scientists. 

References: Jacob T. Vanderplas, Python Data Science Handbook, O'Reilly Media, Inc.,November 2016, ISBN: 9781491912058. 

Wes McKinney, Python for Data Analysis, O’Reilly Media, Inc., October 2012, ISBN:978-1-449-31979-3

Article by: Anuradha Madurapperuma 

anuradhaerandathi@gmail.com

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