Data Visualization Library Research Articles

Penerapan Matplotlib dalam Visualisasi Data untuk Analisis Hubungan Penggunaan Gadget dan Hasil Belajar

This research aims to analyze how the duration of gadget usage impacts students' learning outcomes. The analysis involves creating data visualizations using Python programming with Matplotlib, a widely used data visualization library. The study uses a dataset from 65 students at STMIK Palangkaraya, collected through a Google Forms questionnaire, containing information on gadget use duration and academic performance. The data is prepared and cleaned to ensure accuracy. Python programming with the matplotlib library is then used to visualize the relationship between gadget usage duration and learning outcomes. Data interpretation is conducted based on the visualizations, revealing a significant impact of gadget usage duration on learning outcomes. The results include plots and line plots, demonstrating the impact of gadget usage duration on learning outcomes. This highlights the effectiveness of Matplotlib in interpreting data visualizations to understand the impact of gadget usage duration on learning outcomes.

Crop Yield Prediction using Machine Learning

Weather profoundly impacts agricultural outcomes, making accurate crop prediction vital for farmers' decision-making. This abstract presents a comprehensive overview of weather-based crop prediction, emphasizing its significance, key components, and methodologies. The process begins with the collection and analysis of historical weatherdata encompassing variables such as temperature, precipitation, humidity, and sunlight. Utilizing Python programming and data visualization libraries like Pandas and Matplotlib facilitates the exploration and visualization of this data, revealing trends and patterns. Machine learning algorithms, including regression and ensemble methods, are employedto develop predictive models. These models leverage historical weather data to forecast future crop yields accurately. Python's extensive libraries, such as Scikit-learn and TensorFlow, offer robust tools for model development and evaluation. Incorporating advanced technologies like remote sensing and satellite imagery further refines the prediction process. These tools provide real-time insights into crop health and growth, enhancing the precision of forecasts. Ultimately, weather-based crop prediction serves as a valuable decision support tool for farmers, enabling informed choices regarding planting, irrigation, and harvesting practices. By harnessing historical weather data, machine learning algorithms, and innovative technologies, stakeholders can optimize agricultural productivity, mitigate risks, and contribute to global food security

GLC-Frame: A Framework and Library for Exploration of Multidimensional Data with General Line Coordinates

General Line Coordinates (GLC) are a relatively new set of line-based representations for visualizing multidimensional data with the distinctive characteristics of being reversible and lossless. Given these characteristics, the GLC have a high potential for exploratory multidimensional data analysis, however only partial implementations of some of the GLC techniques are available for the visualization community. In this paper, we present the GLC-Frame, an online exploration tool that supports a dual view and allows users to upload their own dataset and interactively explore the different GLC representations without writing code. We also present the GLC-Vis Library, an open-source data visualization library supporting GLC along with traditional interactions. Finally, we provide a set of usage examples showing how the different techniques behave in both the occlusion and the cluster identification problem. In addition, we present the interactions on GLC representations using the cars dataset. Both the GLC-Frame and the GLC-Vis Library provide an exploration space that will allow the visualization community to use these new techniques and evaluate their potential.

PyComplexHeatmap: a Python package to visualize multimodal genomics data.

Python has emerged as a robust programming language increasingly employed in genomics data analysis, largely due to its comprehensive deep learning libraries and proficiency in handling large-scale data, such as single-cell multi-omics datasets. Although Python has become a prominent data science ecosystem for bioinformatics, there remains a growing demand for advanced heatmap visualization and assembly tools, which are not sufficiently addressed by existing Python-based data visualization libraries. We present PyComplexHeatmap, an all-inclusive Python library for heatmap visualization, inspired by the ComplexHeatmap package currently available in R. PyComplexHeatmap is built upon the matplotlib library and features a versatile, modular interface that seamlessly integrates with other Python-based data science tools, such as Pandas, NumPy, and genomics tools, such as Scanpy, in a standard-compliant manner. This library caters to the requirements of exquisite rendering of multimodal matrix data, incorporating both textual and graphical annotations, thereby enabling efficient integrative analysis of multimodal data and associated metadata.

Assessing the Performance of Python Data Visualization Libraries: A Review

Assessing the Performance of Python Data Visualization Libraries: A Review

АНАЛИЗ ОТРАСЛЕВЫХ ИНДЕКСОВ США, КИТАЯ, ЯПОНИИ, ГЕРМАНИИ И ИНДИИ В ПЕРИОД ПЕРВОГО ГОДА ПАНДЕМИИ КОРОНАВИРУСА НА ОСНОВЕ КРИТЕРИЯ КРАСКЕЛА-УОЛЛИСА

In the work, the movement of the stock market in different countries during the crisis year of 2020 was studied, which did not depend on the decisions made in this country, but obeyed the general trend. In particular, the behavior of sectoral indices of different countries during the first year of the coronavirus was studied and it was concluded that the movement of markets is subject to the global trend. For the analysis, sectoral indices of countries affected to varying degrees by the coronavirus were selected, whose securities markets received various support measures: the United States, Russia, China, Japan and Germany. The impact of the pandemic on industries such as financial services, metallurgy, chemicals, consumer goods and telecommunications is considered. Quotes are considered for the period from 01/01/2020 to 12/31/2020. Graphs of the values of sectoral indices of the Russian Federation and the USA in the specified period are presented and analyzed. Graphs are built in the Jupyter Notebook environment using the Python language and the plotly data visualization library. The concept of hypothesis and criterion is defined and the most important definitions are given. With a decrease in the probability of making a type 1 error, the probability of making a type 2 error increases, so the significance level is chosen so that the power of the criterion is maximum. To test the hypothesis, we used the multivariate KruskalWallis test and its modified version for mediumsized samples with a significance level of α=0.05. The novelty of the study lies in the application of the KruskalWallis criterion to financial data. The study was conducted in Python in the Jupyter Notebook environment using the numpy, pandas, itertools, scipy and plotly libraries.

Data Engineering Pipeline to Analyse Jakarta’s Air Quality during COVID-19-Caused Lockdown Periods

Jakarta lifted up lockdown after passing more than 50 days of large-scale social activity restriction and initiated phase opening to new normal. To analyse Jakarta’s air quality after passing lockdown, a pipeline of data engineering is needed. By acquiring time series data from openaq.com, a time-series database system is developed with Python programming language and its fundamental libraries namely Pandas, NumPy, SQLite. After PM 2.5 data are pre-processed into average per-hour and grouped by applicable periods (pre-lockdown, lockdown, and phase opening), a pattern of PM 2.5 in South Jakarta is revealed by using data visualization library Matplotlib. The apex of PM 2.5 occurs earlier during lockdown (04:00) and phase opening (02:00) rather than when it was normal or pre-lockdown (08:00) even though the nadir of PM2.5 still occurs at the same time (16:00 – 17:00).

Development of software for calculating the results of medicinal substances bioequivalence

International organizations have adopted and recognized drug standards, which should be guided by developers in the field of pharmacology. In practice, there is a problem: how to determine whether a reproduced drug sample really satisfies the world standard for its pharmacological action. It is required to implement an algorithm for checking and confirming the bioequivalence of drugs. The purpose of the study is to automate the verification of standards using an appropriate algorithm, i.e. to develop an application for calculating the results of medicinal substances bioequivalence. The methodology is based on the assumption that the identity in the sense of the created effect of the pharmacokinetic curves of the drug concentration in the blood versus time for the test drug and the standard means their therapeutic equivalence. Research methods include Python programming, use of libraries for data visualization; Visual Studio is an application development environment. The desired algorithm and requirements for the developed application are formulated in the results of the study, based on the study of theoretical issues of determining the medicinal substances bioequivalence. Finally, conclusions about the efficiency of the software are made and options for its improvement are proposed.

Story Analysis Using Natural Language Processing and Interactive Dashboards

ABSTRACT This paper discusses Story Analyzer, which uses a natural language processing (NLP) library and sophisticated data visualization libraries to produce dashboards of interrelated and user-responsive visualizations depicting actors and their interactions in a textual narrative, along with locations, times, and other contexts. Story Analyzer performs information extraction using Stanford’s CoreNLP’s NLP services including sentence recognition, tokenizing, parts-of-speech identification, dependency parsing, named entity recognition, coreference resolution, and temporal tagging. Visualization is done through D3 and scalable vector graphics (SVG) which provide powerful control over gelements and shapes in browser-based user interfaces. Google Charts and Maps are also used for visualizations. Development using NLP for unstructured textual data involves challenges, limitations, and ambiguities that distinguishes it from applications using structured data. Therefore, the paper also discusses issues and limitations inherent with using NLP libraries, and presents workarounds when applied to story analysis. Story Analyzer is applied to a contemporary news article regarding data privacy issues.

Adaptive Choropleth Mapper: An Open-Source Web-Based Tool for Synchronous Exploration of Multiple Variables at Multiple Spatial Extents

Choropleth mapping is an essential visualization technique for exploratory spatial data analysis. Visualizing multiple choropleth maps is a technique that spatial analysts use to reveal spatiotemporal patterns of one variable or to compare the geographical distributions of multiple variables. Critical features for effective exploration of multiple choropleth maps are (1) automated computation of the same class intervals for shading different choropleth maps, (2) dynamic visualization of local variation in a variable, and (3) linking for synchronous exploration of multiple choropleth maps. Since the 1990s, these features have been developed and are now included in many commercial geographic information system (GIS) software packages. However, many choropleth mapping tools include only one or two of the three features described above. On the other hand, freely available mapping tools that support side-by-side multiple choropleth map visualizations are usually desktop software only. As a result, most existing tools supporting multiple choropleth-map visualizations cannot be easily integrated with Web-based and open-source data visualization libraries, which have become mainstream in visual analytics and geovisualization. To fill this gap, we introduce an open-source Web-based choropleth mapping tool called the Adaptive Choropleth Mapper (ACM), which combines the three critical features for flexible choropleth mapping.

Enhancing Data Visualization Capabilities: A Comprehensive Exploration of Seaborn in Python

In the ever-evolving landscape of records technology, the function of powerful information visualization cannot be overstated. As datasets develop in complexity and length, the want for classy gear that could distill meaningful insights from these extensive arrays of records will become more and more important. This studies paper, titled "Enhancing Data Visualization Capabilities: A Comprehensive Exploration of Seaborn in Python," embarks on a adventure to delve deep into the functionalities and programs of Seaborn, a effective and versatile facts visualization library in the Python programming language. The abstract encapsulates the essence of this complete exploration, emphasizing its significance in empowering researchers, analysts, and builders to create visually compelling representations of records. The historical historical past units the stage by using tracing the evolution of data visualization libraries in Python, highlighting the motivations that brought about the emergence of Seaborn. As a foundation, the paper explores the structure and layout standards that underpin Seaborn, presenting readers with a nuanced understanding of its shape and seamless integration with Matplotlib. Building on this foundation, the exploration delves into the world of basic plotting techniques, unraveling how Seaborn facilitates the advent of essential visualizations along with bar plots, histograms, and container plots. A pivotal factor of this studies lies inside the exploration of Seaborn's superior visualization capabilities. This includes an in-intensity examination of complicated strategies like heatmaps, pair plots, and faceting, showcasing how Seaborn stands out in growing informative and visually appealing representations. Furthermore, the paper elucidates Seaborn's integration with Pandas, a cornerstone for efficient records manipulation and analysis. Real-world applications and case research are interwoven all through the narrative, demonstrating how Seaborn transcends theoretical concepts and is carried out in practical eventualities throughout various domains, such as finance, biology, and social sciences. The comparative evaluation gives readers insights into how Seaborn distinguishes itself from different data visualization libraries in Python, emphasizing overall performance, ease of use, and unique use cases in which Seaborn excels. As the exploration maintains, attention is became closer to Seaborn's interactive skills, integration with Jupyter Notebooks, and a plethora of customization options that empower users to craft tailor-made and attractive visualizations. The paper does not turn away from acknowledging demanding situations and obstacles associated with Seaborn, imparting a balanced perspective on its application. The abstract concludes by way of searching toward the destiny, awaiting ongoing tendencies inside the Seaborn library and spotting the collaborative efforts of the network that propels its evolution. In essence, this research paper aspires to be a holistic and enlightening resource, guiding each beginners and seasoned practitioners in harnessing the full capacity of Seaborn for impactful statistics visualization in Python.

Epiviz Web Components: reusable and extensible component library to visualize functional genomic datasets

Interactive and integrative data visualization tools and libraries are integral to exploration and analysis of genomic data. Web based genome browsers allow integrative data exploration of a large number of data sets for a specific region in the genome. Currently available web-based genome browsers are developed for specific use cases and datasets, therefore integration and extensibility of the visualizations and the underlying libraries from these tools is a challenging task. Genomic data visualization and software libraries that enable bioinformatic researchers and developers to implement customized genomic data viewers and data analyses for their application are much needed. Using recent advances in core web platform APIs and technologies including Web Components, we developed the Epiviz Component Library, a reusable and extensible data visualization library and application framework for genomic data. Epiviz Components can be integrated with most JavaScript libraries and frameworks designed for HTML. To demonstrate the ease of integration with other frameworks, we developed an R/Bioconductor epivizrChart package, that provides interactive, shareable and reproducible visualizations of genomic data objects in R, Shiny and also create standalone HTML documents. The component library is modular by design, reusable and natively extensible and therefore simplifies the process of managing and developing bioinformatic applications.

Epiviz Web Components: reusable and extensible component library to visualize functional genomic datasets.

Interactive and integrative data visualization tools and libraries are integral to exploration and analysis of genomic data. Web based genome browsers allow integrative data exploration of a large number of data sets for a specific region in the genome. Currently available web-based genome browsers are developed for specific use cases and datasets, therefore integration and extensibility of the visualizations and the underlying libraries from these tools is a challenging task. Genomic data visualization and software libraries that enable bioinformatic researchers and developers to implement customized genomic data viewers and data analyses for their application are much needed. Using recent advances in core web platform APIs and technologies including Web Components, we developed the Epiviz Component Library, a reusable and extensible data visualization library and application framework for genomic data. Epiviz Components can be integrated with most JavaScript libraries and frameworks designed for HTML. To demonstrate the ease of integration with other frameworks, we developed an R/Bioconductor epivizrChart package, that provides interactive, shareable and reproducible visualizations of genomic data objects in R, Shiny and also create standalone HTML documents.The component library is modular by design, reusable and natively extensible and therefore simplifies the process of managing and developing bioinformatic applications.

GRNsight: a web application and service for visualizing models of small- to medium-scale gene regulatory networks

GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. The original motivation came from our efforts to perform parameter estimation and forward simulation of the dynamics of a differential equations model of a small GRN with 21 nodes and 31 edges. We wanted a quick and easy way to visualize the weight parameters from the model which represent the direction and magnitude of the influence of a transcription factor on its target gene, so we created GRNsight. GRNsight automatically lays out either an unweighted or weighted network graph based on an Excel spreadsheet containing an adjacency matrix where regulators are named in the columns and target genes in the rows, a Simple Interaction Format (SIF) text file, or a GraphML XML file. When a user uploads an input file specifying an unweighted network, GRNsight automatically lays out the graph using black lines and pointed arrowheads. For a weighted network, GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on the sign (positive for activation or negative for repression) and magnitude of the weight parameter. GRNsight is written in JavaScript, with diagrams facilitated by D3.js, a data visualization library. Node.js and the Express framework handle server-side functions. GRNsight’s diagrams are based on D3.js’s force graph layout algorithm, which was then extensively customized to support the specific needs of GRNs. Nodes are rectangular and support gene labels of up to 12 characters. The edges are arcs, which become straight lines when the nodes are close together. Self-regulatory edges are indicated by a loop. When a user mouses over an edge, the numerical value of the weight parameter is displayed. Visualizations can be modified by sliders that adjust the force graph layout parameters and through manual node dragging. GRNsight is best-suited for visualizing networks of fewer than 35 nodes and 70 edges, although it accepts networks of up to 75 nodes or 150 edges. GRNsight has general applicability for displaying any small, unweighted or weighted network with directed edges for systems biology or other application domains. GRNsight serves as an example of following and teaching best practices for scientific computing and complying with FAIR principles, using an open and test-driven development model with rigorous documentation of requirements and issues on GitHub. An exhaustive unit testing framework using Mocha and the Chai assertion library consists of around 160 automated unit tests that examine nearly 530 test files to ensure that the program is running as expected. The GRNsight application (http://dondi.github.io/GRNsight/) and code (https://github.com/dondi/GRNsight) are available under the open source BSD license.

Review of Methodological Primitives for the Phased Construction of Data Visualization Model

The paper considers common methodological primitives by the example of phased data visualization model construction with prepared data. The purpose of the article is to form a comprehensive vision of the visualization model creation. The primitives classification and communication between them are suggested. The paper also considers data visualization libraries in popular programming languages.

Visualization with Charting Library Based on SVG for Amrita Dynamic Dashboard

Abstract Data Visualization is the representation of data in a graphical or pictorial format. For the effective communication of data for a user proper visualization is necessary. Visualization is essential in order for the user to understand the data in an easy way. Visualization of data is done through various charts that represent the attributes of the data. For web applications, there are many open source JavaScript libraries that work on HTML5 (using SVG or CANVAS). But the drawback of these libraries is that they don’t provide for much flexibility with respect to configuration. They also don’t provide generalization of charts. Also many data mining algorithms are not supported by these libraries for data visualization. This paper has illustrated in building JavaScript charting libraries that would ensure proper visualization of data which is flexible for user customization. The charting library supports different types of charts varying from scatter chart to line chart to bar chart that are used for various algorithms. The libraries are built based on Object-Oriented JavaScript concept to support web applications that run either on the internet or intranet, so that extending the same in the future is also possible.

Power-Consumption Analysis through Web-Based Visual Data Exploration

Abstract In recent years, the increasing capabilities of current technologies have made the acquisition and storage of large datasets an easy task. However, most times their unmanageable size, along with their complexity makes it a challenge to handle and analyze them. The emerging field of visual analytics relies on visualization principles and interactive interfaces to provide ways for amplifying human cognition, so that the information processing is improved and a better understanding of these datasets is achieved. In addition, current technologies to develop visual interfaces allow to develop powerful interaction mechanisms that enable the user to be an active part in the process. In this paper, data visualization foundations and web-based methods are considered for user-driven supervision tasks in decision-support systems. A real case consisting in the analysis of electric power demand in university buildings is presented via a web application developed using the recent data visualization library called D3. Time-series visualization and similarity maps are represented along with interactive techniques, allowing a dynamic data exploration.