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This book is designed to show readers the concepts of Python 3 programming and the art of data visualization. It also explores cutting-edge techniques using ChatGPT/GPT-4 in harmony with Python for generating visuals that tell more compelling data stories. Chapter 1 introduces the essentials of Python, covering a vast array of topics from basic data types, loops, and functions to more advanced constructs like dictionaries, sets, and matrices. In Chapter 2, the focus shifts to NumPy and its powerful array operations, leading into data visualization using prominent libraries such as Matplotlib. Chapter 6 includes Seaborn's rich visualization tools, offering insights into datasets like Iris and Titanic. Further, the book covers other visualization tools and techniques, including SVG graphics, D3 for dynamic visualizations, and more. Chapter 7 covers information about the main features of ChatGPT and GPT-4, as well as some of their competitors. Chapter 8 contains examples of using ChatGPT in order to perform data visualization, such as charts and graphs that are based on datasets (e.g., the Titanic dataset). Companion files with code, datasets, and figures are available for downloading. From foundational Python concepts to the intricacies of data visualization, this book is ideal for Python practitioners, data scientists, and anyone in the field of data analytics looking to enhance their storytelling with data through visuals. It's also perfect for educators seeking material for teaching advanced data visualization techniques.

In recent years, researchers at home and abroad have accumulated a lot of experience in the research of data visualization technology, and they have played animportant role in scientific discovery, medical diagnosis, business decision-making, and engineering applications. As a library developed using Python language, Matplotlib has a concise language, high drawing accuracy, and simple and easy-to-understand code. This article first introduces data visualization and related technologies used and then uses Python's Matplotlib library and pyecharts library to realize data visualization. Through representative examples, combined with the use of correct charts, visual processing of data in different fields, so as to further analyze the effect of visualization.

Python is a robust, procedural, object-oriented, and functional language. The features of the language make it valuable for web development, game development, business, and scientific programming. This book deals with problem-solving and programming in Python. It concentrates on the development of efficient algorithms, the syntax of the language, and the ability to design programs in order to solve problems. In addition to standard Python topics, the book has extensive coverage of NumPy, data visualization, and Matplotlib. Numerous types of exercises, including theoretical, programming, and multiple-choice, reinforce the concepts covered in each chapter.

In the meantime, startup companies are rising more and more. Companies want to track their prominence and their advancement. Basically, the whole data need to be found and stored in database. They need to check their company's turnout-catastrophe ratio and their expansion. The company obliged to know how compact it is, with different companies. Since the data is in huge amount, estimating the data is almost difficult. Soon these terms, physically reckoning may become annoyed. Considering the plot, we have a proposal termed visualization. Visualization is the graphical depiction of data in the pattern of graphs, tabulations, charts by which we could know about company widening. Using data visualization, we could form an opinion on company's stand/rate. So to evolve and to develop web pages, the automations we used are html, css for the data analysis, python modules are used. Few of them are pandas, seaborn, matplotlib, plotly. Implementing the data visualization using IDE's like jupyter, Sublime Text makes the effort transparent. But integrating web pages with python modules will be the challenging load. So to carry out this, we use DJANGO Framework to visualize the data. DJANGO affords frontend, back-end and database This Data visualization is intended to evolve web pages and scrutinize the data.

Social and information networks undermine the real relationship between the individuals (ego) and the friends (alters) they are connected with on social media. The structure of individual network is highlighted by the ego network. Egocentric approach is popular due to its focus on individuals, groups, or communities. Size, structure, and composition directly impact the ego networks. Moreover, analysis includes strength of ego – alter ties degree and strength of ties. Degree gives the first overview of network. Social support in the network is explored with the “gap” between the degree and average strength. These outcomes firmly propose that, regardless of whether the approaches to convey and to keep up social connections are evolving because of the dispersion of online social networks, the way individuals sort out their social connections appears to remain unaltered. As online social networks evolve, they help in receiving more diverse information.

The G Protein-Coupled Receptor (GPCR) family consists of more than 800 different members. In this article, we attempt to use the physicochemical properties of Composition, Transition, Distribution (CTD) to represent GPCRs. The dimensionality reduction method of MRMD2.0 filters the physicochemical properties of GPCR redundancy. Matplotlib plots the coordinates to distinguish GPCRs from other protein sequences. The chart data show a clear distinction effect, and there is a well-defined boundary between the two. The experimental results show that our method can predict GPCRs.

In an increasingly technology-dependent world, it is not surprising that STEM (Science, Technology, Engineering, and Mathematics) graduates are in high demand. This state of affairs, however, has made the public overlook the case that not only computing and artificial intelligence are naturally interdisciplinary, but that a huge portion of generated data comes from human–computer interactions, thus they are social in character and nature. Hence, social science practitioners should be in demand too, but this does not seem the case. One of the reasons for such a situation is that political and social science departments worldwide tend to remain in their “comfort zone” and see their disciplines quite traditionally, but by doing so they cut themselves off from many positions today. The authors believed that these conditions should and could be changed and thus in a few years created a specifically tailored course for students in Political Science. This paper examines the experience of the last year of such a program, which, after several tweaks and adjustments, is now fully operational. The results and students’ appreciation are quite remarkable. Hence the authors considered the experience was worth sharing, so that colleagues in social and political science departments may feel encouraged to follow and replicate such an example.

Bottom-up systems biology entails the construction of kinetic models of cellular pathways by collecting kinetic information on the pathway components (e.g., enzymes) and collating this into a kinetic model, based for example on ordinary differential equations. This requires integration and data transfer between a variety of tools, ranging from data acquisition in kinetics experiments, to fitting and parameter estimation, to model construction, evaluation and validation. Here, we present a workflow that uses the Python programming language, specifically the modules from the SciPy stack, to facilitate this task. Starting from raw kinetics data, acquired either from spectrophotometric assays with microtitre plates or from Nuclear Magnetic Resonance (NMR) spectroscopy time-courses, we demonstrate the fitting and construction of a kinetic model using scientific Python tools. The analysis takes place in a Jupyter notebook, which keeps all information related to a particular experiment together in one place and thus serves as an e-labbook, enhancing reproducibility and traceability. The Python programming language serves as an ideal foundation for this framework because it is powerful yet relatively easy to learn for the non-programmer, has a large library of scientific routines and active user community, is open-source and extensible, and many computational systems biology software tools are written in Python or have a Python Application Programming Interface (API). Our workflow thus enables investigators to focus on the scientific problem at hand rather than worrying about data integration between disparate platforms.

The aim of the author of this article is to show the users of Geographical Information Systems how to present the distortions in a simple way. The intention of the author is also to popularize the knowledge in the scope of map projections and to inform the users of the maps that, despite all the advanced modern tools, an elimination of the problem, connected with the map projections and cartographical distortions occurring in them, has failed so far. The author presents a brief overview of the measures in the scope of distortions and methods of their presentation. It is also shown how the users can generate the maps, presenting a distortion by themselves. It is much easier to perform this type of visualization with the help of “matplotlib library”, basically everyone can compile such a map.

The NEURON simulation environment is a commonly used tool to perform electrical simulation of neurons and neuronal networks. The NEURON User Interface, based on the now discontinued InterViews library, provides some limited facilities to explore models and to plot their simulation results. Other limitations include the inability to generate a three-dimensional visualization, no standard mean to save the results of simulations, or to store the model geometry within the results. Neuronvisio (http://neuronvisio.org) aims to address these deficiencies through a set of well designed python APIs and provides an improved UI, allowing users to explore and interact with the model. Neuronvisio also facilitates access to previously published models, allowing users to browse, download, and locally run NEURON models stored in ModelDB. Neuronvisio uses the matplotlib library to plot simulation results and uses the HDF standard format to store simulation results. Neuronvisio can be viewed as an extension of NEURON, facilitating typical user workflows such as model browsing, selection, download, compilation, and simulation. The 3D viewer simplifies the exploration of complex model structure, while matplotlib permits the plotting of high-quality graphs. The newly introduced ability of saving numerical results allows users to perform additional analysis on their previous simulations.