Five Popular Python Libraries for Data Science

Five Popular Python Libraries for Data Science
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by Sanjeev Kapoor 09 Oct 2019

The Python programming language is currently one of the five most popular worldwide. Its popularity has been rising at a steady pace for more than fifteen years. There are two main reasons for this rise: First Python is extremely simple to learn, which is the reason why it is gradually becoming the most widespread language used in the scope of introductory programming university courses. Second, Python is an excellent choice when it comes building some of most cool and trendy systems nowadays, such as Big Data, Artificial Intelligence, Robotics and Cyber Security systems. Likewise, Python is considered one of the best languages for data science and its popularity among data scientists has been growing faster than other machine learning and data mining languages like R, Julia and Java. Python provides excellent features for loading, transforming, analyzing and visualizing datasets. The latter features are provided by a number of libraries that extend the Python’s basics in the above listed directions.


NumPy: Scientific Computations with Python

NumPy is a Python library that facilitates the execution of scientific computations with Python. It stands for Numeric Python and offers a host of functionalities for creating and manipulating arrays, but also for transforming arrays to other arrays based on the application of various operators. Moreover, it enables one to work with trigonometric functions (e.g., sine, cosine, tangent) and hyberbolic functions, while rounding numbers as well. In general, the more one knows about scientific computations, the more will enjoy and find useful NumPy. Given that data science involves many scientific and statistical functions, this library is very widely used among data scientists.

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Pandas: Working with Data Structures and Analysing Datasets

Almost every data scientist using Python takes advantage of the Pandas library. Pandas provides a range of high performance, easy-to-use and developer friendly tools for loading and manipulating datasets. Pandas main construct is the “data frame”, which is a data structure that is used to store and process data. Using “data frames”, data scientists can take advantage of Pandas in order to load, manage and explore datasets in fast and efficient ways. Data frames can be also used for organizing and labeling data using Pandas alignment and indexing functionalities. Pandas is also very versatile for performing data preprocessing and data preparation tasks such as handling missing data value or converting data to agreed homogeneous forms. Likewise, one can used Pandas to clean up messy data, which is another commonly performed data preparation and preprocessing task. Clean data are usually much easier to understand, while providing a basis for more organized and better structured Python software code.

Pandas is very handy when it comes to reading and writing data from various sources. In particular, it facilitates reading and writing data not only into data structures, but also into web services, databases and other repositories or sinks. Pandas provides a range of “inbuilt” tools, which are destined to boost data input/output and data read/write operations. Using Pandas such operations take place based on a fraction of the code that would be needed for performing exactly the same tasks in other languages.  Moreover, Pandas supports multiple file formats such as JSON, XML, XLS and CVS formats. This is a very powerful feature for developers, who typically waste time and effort doing conversion between different formats. In the Big Data era, where data stem from a plethora of heterogeneous sources, Pandas support for multiple formats is therefore a salient feature for most developers.  Finally, Pandas is also very efficient in merging and joining datasets i.e. in performing the most common transformations needed for obtaining consolidated datasets. The latter is usually what data scientists need prior to starting their analytics tasks.


Seaborn: Visualizing Datasets

Data scientists need to visualize their datasets, as a means of inspecting intermediate and/or final outcomes of their data analysis. Seaborn is a python library for data visualization that enables data scientists to draw graphs based on statistical information. It is based on Matplotlib, which is a 2D plotting python library and enables the creation of various plots in different environments. It is an alternate to seaborn, and seaborn is based on matplotlib. Seaborn comes with a set of APIs that facilitate inspection of relationships between libraries, as well as specialized support for displaying observations or aggregate statistics. It provides also the means for visualizing univariate or bivariate distributions, but also for comparing them across different subsets of data. Moreover, using Seabron one can plot linear regression models, which are very commonly used in data analysis. In terms of the appearance of the various plots, the library provides a range of color palettes that can be associated with different trends and patterns on the data.


SciKit learn: Building Machine Learning Models in Python

Following the tasks of loading, pre-processing, preparing and consolidating datasets, data scientists will attempt to extract knowledge from their data by means of building and evaluating some machine learning model. To this end, they can use the SciKit learn library, an open source, easy-to-use Python module that facilitates the development of machine learning models (i.e Python for Machine Learning). SciKit learn leverages other libraries such as the above-listed NumPy and matplotlib. It provides a range of simple and efficient tools for the most common data mining tasks. Specifically, it provides support for tasks like:

  • Classification i.e. models that identify to which category an object belongs, based on algorithms like Support Vector Machines (SVM), nearest neighbors, random forest and more.
  • Regression i.e. models that predicting a continuous-valued attribute associated with an object. Regression is performed based on algorithms like Support Vector Regression (SVR), Ridge Regression and Lasso.
  • Clustering, which falls in the realm of unsupervised learning techniques that can automatically group similar objects into sets of related objects. In this direction, SciKit learn enables data scientists to use algorithms such as K-Means, spectral clustering and Mean-Shift.
  • Dimensionality reduction, which aims at reducing the number of random variables to consider, leveraging on algorithms like Principal Component Analysis (PCA), feature selection and non-negative matrix factorization.

Note also that SciKit learn facilitates the process of model selection, which involves comparing, validating and choosing parameters and models.


Jupyter: An Interactive Environment for Python Development

Data scientists need also an interactive and integrated environment for using the above-listed libraries and implementing their applications. This is provided by JupyterLab, which is a web-based interactive development environment for Jupyter notebooks, code, and data. The JupyterLab module provides flexibility in configuring and customizing its user interface to the needs of data scientists. This configuration can support various data science workflows in-line with the data science methodology used. Also, JupyterLab is extensible and modular, as it allows developers to write new components as plugins and accordingly to flexibly integrate them in the notebook.


A closer look at the above-listed libraries and modules provides enough evidence about the popularity of Python among data scientists. These libraries boost simplicity and developers’ productivity, while ensuring coverage of the full range of development activities that are entailed in the data mining and knowledge extraction process. For example, the presented Python libraries can be used to support all the phases of the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology for machine learning, which we have extensively presented in earlier blog posts. We therefore expect the number of data scientists that use Python for their endeavours to grow further in the next few years.

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