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Unlock deeper insights into visualization in form of 2D and 3D graphs using Matplotlib 2.x About This Book Create and customize live graphs, by adding style, color, font to make appealing graphs. A complete guide with insightful use cases and examples to perform data visualizations with Matplotlib's extensive toolkits. Create timestamp data visualizations on 2D and 3D graphs in form of plots, histogram, bar charts, scatterplots and more. Who This Book Is For This book is for anyone interested in data visualization, to get insights from big data with Python and Matplotlib 2.x. With this book you will be able to extend your knowledge and learn how to use python code in order to visualize your data with Matplotlib. Basic knowledge of Python is expected. What You Will Learn Familiarize with the latest features in Matplotlib 2.x Create data visualizations on 2D and 3D charts in the form of bar charts, bubble charts, heat maps, histograms, scatter plots, stacked area charts, swarm plots and many more. Make clear and appealing figures for scientific publications. Create interactive charts and animation. Extend the functionalities of Matplotlib with third-party packages, such as Basemap, GeoPandas, Mplot3d, Pandas, Scikit-learn, and Seaborn. Design intuitive infographics for effective storytelling. In Detail Big data analytics are driving innovations in scientific research, digital marketing, policy-making and much more. Matplotlib offers simple but powerful plotting interface, versatile plot types and robust customization. Matplotlib 2.x By Example illustrates the methods and applications of various plot types through real world examples. It begins by giving readers the basic know-how on how to create and customize plots by Matplotlib. It further covers how to plot different types of economic data in the form of 2D and 3D graphs, which give insights from a deluge of data from public repositories, such as Quandl Finance. You will learn to visualize geographical data on maps and implement interactive charts. By the end of this book, you will become well versed with Matplotlib in your day-to-day work to perform advanced data visualization. This book will guide you to prepare high quality figures for manuscripts and presentations. You will learn to create intuitive info-graphics and reshaping your message crisply understandable. Style and approach Step by step comprehensive guide filled with real world examples.
Unlock deeper insights into visualization in form of 2D and 3D graphs using Matplotlib 2.xAbout This Book* Create and customize live graphs, by adding style, color, font to make appealing graphs.* A complete guide with insightful use cases and examples to perform data visualizations with Matplotlib's extensive toolkits.* Create timestamp data visualizations on 2D and 3D graphs in form of plots, histogram, bar charts, scatterplots and more.Who This Book Is ForThis book is for anyone interested in data visualization, to get insights from big data with Python and Matplotlib 2.x. With this book you will be able to extend your knowledge and learn how to use python code in order to visualize your data with Matplotlib. Basic knowledge of Python is expected.What You Will Learn* Familiarize with the latest features in Matplotlib 2.x* Create data visualizations on 2D and 3D charts in the form of bar charts, bubble charts, heat maps, histograms, scatter plots, stacked area charts, swarm plots and many more.* Make clear and appealing figures for scientific publications.* Create interactive charts and animation.* Extend the functionalities of Matplotlib with third-party packages, such as Basemap, GeoPandas, Mplot3d, Pandas, Scikit-learn, and Seaborn.* Design intuitive infographics for effective storytelling.In DetailBig data analytics are driving innovations in scientific research, digital marketing, policy-making and much more. Matplotlib offers simple but powerful plotting interface, versatile plot types and robust customization.Matplotlib 2.x By Example illustrates the methods and applications of various plot types through real world examples.It begins by giving readers the basic know-how on how to create and customize plots by Matplotlib. It further covers how to plot different types of economic data in the form of 2D and 3D graphs, which give insights from a deluge of data from public repositories, such as Quandl Finance. You will learn to visualize geographical data on maps and implement interactive charts.By the end of this book, you will become well versed with Matplotlib in your day-to-day work to perform advanced data visualization. This book will guide you to prepare high quality figures for manuscripts and presentations. You will learn to create intuitive info-graphics and reshaping your message crisply understandable.Style and approachStep by step comprehensive guide filled with real world examples.
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Understand and build beautiful and advanced plots with Matplotlib and Python Key FeaturesPractical guide with hands-on examples to design interactive plotsAdvanced techniques to constructing complex plotsExplore 3D plotting and visualization using Jupyter NotebookBook Description In this book, you’ll get hands-on with customizing your data plots with the help of Matplotlib. You’ll start with customizing plots, making a handful of special-purpose plots, and building 3D plots. You’ll explore non-trivial layouts, Pylab customization, and more about tile configuration. You’ll be able to add text, put lines in plots, and also handle polygons, shapes, and annotations. Non-Cartesian and vector plots are exciting to construct, and you’ll explore them further in this book. You’ll delve into niche plots and visualize ordinal and tabular data. In this book, you’ll be exploring 3D plotting, one of the best features when it comes to 3D data visualization, along with Jupyter Notebook, widgets, and creating movies for enhanced data representation. Geospatial plotting will also be explored. Finally, you’ll learn how to create interactive plots with the help of Jupyter. Learn expert techniques for effective data visualization using Matplotlib 3 and Python with our latest offering -- Matplotlib 3.0 Cookbook What you will learnDeal with non-trivial and unusual plotsUnderstanding Basemap methodsCustomize and represent data in 3D Construct Non-Cartesian and vector plotsDesign interactive plots using Jupyter NotebookMake movies for enhanced data representationWho this book is for This book is aimed at individuals who want to explore data visualization techniques. A basic knowledge of Matplotlib and Python is required.
Build attractive, insightful, and powerful visualizations to gain quality insights from your data Key FeaturesMaster Matplotlib for data visualizationCustomize basic plots to make and deploy figures in cloud environmentsExplore recipes to design various data visualizations from simple bar charts to advanced 3D plotsBook Description Matplotlib provides a large library of customizable plots, along with a comprehensive set of backends. Matplotlib 3.0 Cookbook is your hands-on guide to exploring the world of Matplotlib, and covers the most effective plotting packages for Python 3.7. With the help of this cookbook, you'll be able to tackle any problem you might come across while designing attractive, insightful data visualizations. With the help of over 150 recipes, you'll learn how to develop plots related to business intelligence, data science, and engineering disciplines with highly detailed visualizations. Once you've familiarized yourself with the fundamentals, you'll move on to developing professional dashboards with a wide variety of graphs and sophisticated grid layouts in 2D and 3D. You'll annotate and add rich text to the plots, enabling the creation of a business storyline. In addition to this, you'll learn how to save figures and animations in various formats for downstream deployment, followed by extending the functionality offered by various internal and third-party toolkits, such as axisartist, axes_grid, Cartopy, and Seaborn. By the end of this book, you'll be able to create high-quality customized plots and deploy them on the web and on supported GUI applications such as Tkinter, Qt 5, and wxPython by implementing real-world use cases and examples. What you will learnDevelop simple to advanced data visualizations in Matplotlib Use the pyplot API to quickly develop and deploy different plots Use object-oriented APIs for maximum flexibility with the customization of figuresDevelop interactive plots with animation and widgets Use maps for geographical plotting Enrich your visualizations using embedded texts and mathematical expressionsEmbed Matplotlib plots into other GUIs used for developing applicationsUse toolkits such as axisartist, axes_grid1, and cartopy to extend the base functionality of MatplotlibWho this book is for The Matplotlib 3.0 Cookbook is for you if you are a data analyst, data scientist, or Python developer looking for quick recipes for a multitude of visualizations. This book is also for those who want to build variations of interactive visualizations.
This is a practical, hands-on book, with a lot of code and images. It presents the real code that generates every image and describes almost every single line of it, so that you know exactly what's going on. Introductory, descriptive, and theoretical parts are mixed with examples, so that reading and understanding them is easy. All of the examples build gradually with code snippets, their explanations, and plot images where necessary with the complete code and output presented at the end. This book is essentially for Python developers who have a good knowledge of Python; no knowledge of Matplotlib is required. You will be creating 2D plots using Matplotlib in no time at all.
Understand, explore, and effectively present data using the powerful data visualization techniques of Python Key FeaturesUse the power of Pandas and Matplotlib to easily solve data mining issuesUnderstand the basics of statistics to build powerful predictive data modelsGrasp data mining concepts with helpful use-cases and examplesBook Description Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining. You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models. By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional. This Learning Path includes content from the following Packt products: Statistics for Machine Learning by Pratap DangetiMatplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin YimPandas Cookbook by Theodore PetrouWhat you will learnUnderstand the statistical fundamentals to build data modelsSplit data into independent groups Apply aggregations and transformations to each groupCreate impressive data visualizationsPrepare your data and design models Clean up data to ease data analysis and visualizationCreate insightful visualizations with Matplotlib and SeabornCustomize the model to suit your own predictive goalsWho this book is for If you want to learn how to use the many libraries of Python to extract impactful information from your data and present it as engaging visuals, then this is the ideal Learning Path for you. Some basic knowledge of Python is enough to get started with this Learning Path.
The book covers data science topics in Python language. Libraries such as Numpy, Matplotlib, Pandas, Scipy are explained in detail. In addition to data science, the book contains the usage of many libraries for developers of Python. The basic knowledge needed to use Artificial Intelligence, Machine Learning, Natural Language Processing, Computer Vision features are covered. The book contains tools for data analysis and business intelligence.
Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. What You'll Learn Work with vectors and matrices using NumPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Review statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its related ecosystem for numerical computing.
The academic field of computer science did not develop as a separate subject of study until the 1960s after it had been in existence since the 1950s. The mathematical theory that underpinned the fields of computer programming, compilers, and operating systems was one of the primary focuses of this class. Other important topics were the various programming languages and operating systems. Context-free languages, finite automata, regular expressions, and computability were a few of the topics that were discussed in theoretical computer science lectures. The area of study known as algorithmic analysis became an essential component of theory in the 1970s, after having been mostly overlooked for the majority of its existence up to that point in time. The purpose of this initiative was to investigate and identify practical applications for computer technology. At the time, a significant change is taking place, and a greater amount of attention is being paid to the vast number of different applications that may be utilized. This shift is the cumulative effect of several separate variables coming together at the same time. The convergence of computing and communication technology has been a major motivator, and as a result, this change may be primarily attributed to that convergence. Our current knowledge of data and the most effective approach to work with it in the modern world has to be revised in light of recent advancements in the capability to monitor, collect, and store data in a variety of fields, including the natural sciences, business, and other fields. This is necessary because of the recent breakthroughs in these capabilities. This is as a result of recent advancements that have been made in these capacities. The widespread adoption of the internet and other forms of social networking as indispensable components of people's lives brings with it a variety of opportunities for theoretical development as well as difficulties in actual use. Traditional subfields of computer science continue to hold a significant amount of weight in the field as a whole; however, researchers of the future will focus more on how to use computers to comprehend and extract usable information from massive amounts of data arising from applications rather than how to make computers useful for solving particular problems in a well-defined manner. This shift in emphasis is due to the fact that researchers of 1 | P a ge the future will be more concerned with how to use computers to comprehend and extract usable information from massive amounts of data arising from applications. This shift in emphasis is because researchers of the future will be more concerned with how to use the information they find. As a result of this, we felt it necessary to compile this book, which discusses a theory that would, according to our projections, play an important role within the next 40 years. We think that having a grasp of this issue will provide students with an advantage in the next 40 years, in the same way that having an understanding of automata theory, algorithms, and other topics of a similar sort provided students an advantage in the 40 years prior to this one, and in the 40 years after this one. A movement toward placing a larger emphasis on probabilities, statistical approaches, and numerical processes is one of the most significant shifts that has taken place as a result of the developments that have taken place. Early drafts of the book have been assigned reading at a broad variety of academic levels, ranging all the way from the undergraduate level to the graduate level. The information that is expected to have been learned before for a class that is taken at the undergraduate level may be found in the appendix. As a result of this, the appendix will provide you with some activities to do as a component of your project.