Download or read book Fundamentals of Data Visualization written by Claus O. Wilke and published by O'Reilly Media. This book was released on 2019-03-18 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color as a tool to highlight, distinguish, or represent a value Understand the importance of redundant coding to ensure you provide key information in multiple ways Use the book’s visualizations directory, a graphical guide to commonly used types of data visualizations Get extensive examples of good and bad figures Learn how to use figures in a document or report and how employ them effectively to tell a compelling story

Download or read book Python Data Visualization Essentials Guide written by Kallur Rahman and published by BPB Publications. This book was released on 2021-07-30 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build your data science skills. Start data visualization Using Python. Right away. Become a good data analyst by creating quality data visualizations using Python. KEY FEATURES ● Exciting coverage on loads of Python libraries, including Matplotlib, Seaborn, Pandas, and Plotly. ● Tons of examples, illustrations, and use-cases to demonstrate visual storytelling of varied datasets. ● Covers a strong fundamental understanding of exploratory data analysis (EDA), statistical modeling, and data mining. DESCRIPTION Data visualization plays a major role in solving data science challenges with various capabilities it offers. This book aims to equip you with a sound knowledge of Python in conjunction with the concepts you need to master to succeed as a data visualization expert. The book starts with a brief introduction to the world of data visualization and talks about why it is important, the history of visualization, and the capabilities it offers. You will learn how to do simple Python-based visualization with examples with progressive complexity of key features. The book starts with Matplotlib and explores the power of data visualization with over 50 examples. It then explores the power of data visualization using one of the popular exploratory data analysis-oriented libraries, Pandas. The book talks about statistically inclined data visualization libraries such as Seaborn. The book also teaches how we can leverage bokeh and Plotly for interactive data visualization. Each chapter is enriched and loaded with 30+ examples that will guide you in learning everything about data visualization and storytelling of mixed datasets. WHAT YOU WILL LEARN ● Learn to work with popular Python libraries and frameworks, including Seaborn, Bokeh, and Plotly. ● Practice your data visualization understanding across numerous datasets and real examples. ● Learn to visualize geospatial and time-series datasets. ● Perform correlation and EDA analysis using Pandas and Matplotlib. ● Get to know storytelling of complex and unstructured data using Bokeh and Pandas. ● Learn best practices in writing clean and short python scripts for a quicker visual summary of datasets. WHO THIS BOOK IS FOR This book is for all data analytics professionals, data scientists, and data mining hobbyists who want to be strong data visualizers by learning all the popular Python data visualization libraries. Prior working knowledge of Python is assumed. TABLE OF CONTENTS 1. Introduction to Data Visualization 2. Why Data Visualization 3. Various Data Visualization Elements and Tools 4. Using Matplotlib with Python 5. Using NumPy and Pandas for Plotting 6. Using Seaborn for Visualization 7. Using Bokeh with Python 8. Using Plotly, Folium, and Other Tools for Data Visualization 9. Hands-on Examples and Exercises, Case Studies, and Further Resources

Download or read book Fundamentals of Statistics and Visualization in Python written by Karen Yang and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to display your data using Python's visualization tools About This Video Discover and sharpen your skills in core statistics and visualization Create vibrant data visualizations using Seaborn and Matplotlib Apply what you've learned from this course to your data analysis projects In Detail Statistics and visualization in Python can be applied to a wide variety of areas; having these skills is crucial for data scientists. In this course, we explore several core statistical concepts to utilize data; construct confidence intervals in Python and assess the results; discover correlations; and update your beliefs using Bayesian Inference. In this tutorial, you will discover how to use the Statsmodels, Matplotlib, pandas, and Seaborn Python libraries for statistical data visualization. Follow along with author--Dr. Karen Yang, a seasoned data scientist and data engineer--to explore, learn, and strengthen your skills in fundamental statistics and visualization. This course utilizes the Jupyter Notebook environment to execute tasks. By the end of this learning journey, you'll have developed a solid understanding of fundamental statistics and visualization concepts and will be confident enough to apply them to your data analysis projects. Please note that prior knowledge of Python programming and some familiarity with pandas and NumPy are needed in order to get the best out of this course.

Download or read book Data Visualization with Python and JavaScript written by Kyran Dale and published by "O'Reilly Media, Inc.". This book was released on 2016-06-30 with total page 581 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to turn raw data into rich, interactive web visualizations with the powerful combination of Python and JavaScript. With this hands-on guide, author Kyran Dale teaches you how build a basic dataviz toolchain with best-of-breed Python and JavaScript libraries—including Scrapy, Matplotlib, Pandas, Flask, and D3—for crafting engaging, browser-based visualizations. As a working example, throughout the book Dale walks you through transforming Wikipedia’s table-based list of Nobel Prize winners into an interactive visualization. You’ll examine steps along the entire toolchain, from scraping, cleaning, exploring, and delivering data to building the visualization with JavaScript’s D3 library. If you’re ready to create your own web-based data visualizations—and know either Python or JavaScript— this is the book for you. Learn how to manipulate data with Python Understand the commonalities between Python and JavaScript Extract information from websites by using Python’s web-scraping tools, BeautifulSoup and Scrapy Clean and explore data with Python’s Pandas, Matplotlib, and Numpy libraries Serve data and create RESTful web APIs with Python’s Flask framework Create engaging, interactive web visualizations with JavaScript’s D3 library

Download or read book Python Data Visualization written by Samuel Burns and published by . This book was released on 2019-10-22 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Visualization is the presentation of data in graphical format.In this tutorial for beginners, you will learn how to present data graphically with Python, Matplotlib, and Seaborn. If you need a short book to master data vizualisation from scratch, this guide is for you. Get your copy now!!!Book ObjectivesThis book is an exploration of data visualization in Python programming language. Here are the objectives of the book: To help you understand the need for data visualization and appreciate its power in data analysis. To help you learn the various types of plots that you can create to visualize your data. To help you know the various tools that you can use for data visualization, including basic, specialized and advanced tools. To help you make the right decision in choosing the tool and the kind of plot to use to visualize your data. To help you learn the power of Python in data visualization. To equip you with data visualization skills in Python programming language. To help you learn the various Python libraries that you can use for data visualization. Who this Book is for? The author targets the following groups of people: Anyone who needs to know the need for data visualization in an organization. Any individual who needs to know the various tools they can use for data visualization. Any individual who needs to know the various types of graphics they can use to represent their data and how to interpret the graphics. Anybody who needs to learn data visualization in Python using various libraries such as Pandas, Matplotlib, Seaborn and Folium. Anyone who needs to learn how to visualize different types of data including textual, numerical and geospatial data. RequirementsThe author expects you to have a computer installed with an operating system such as Linux, Windows or Mac OS X. What is inside the book? BASICS OF DATA VISUALIZATION BASIC AND SPECIALIZED DATA VISUALIZATION TOOLS ADVANCED VISUALIZATIONS TOOLSEXPLORING THE LIBRARIES DATA VISUALIZATION WITH MATPLOTLIBDATA VISUALIZATION WITH PANDAS DATA VISUALIZATION WITH SEABORN CREATING MAPS AND VISUALIZING GEOSPATIAL DATA The author has discussed everything related to data visualization. You are first familiarized with the fundamentals of data visualization to help you know what it is and why it is of importance to any organization. The author has then discussed the various types of tools that can be used for data visualization. These tools include the basic, specialized and advanced ones. Practically, the author focuses on how to visualize data in the Python programming language. The process of plotting different types of data using different types of plots has been discussed. You will learn how to plot textual, numerical and geospatial data in Python using different libraries such as Pandas, Matplotlib, Seaborn and Folium. Python codes have been provided alongside images of the expected outputs and the corresponding code descriptions.

Download or read book Data Visualization written by Kieran Healy and published by Princeton University Press. This book was released on 2018-12-18 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible primer on how to create effective graphics from data This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way. Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible. Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings. Provides hands-on instruction using R and ggplot2 Shows how the “tidyverse” of data analysis tools makes working with R easier and more consistent Includes a library of data sets, code, and functions

Download or read book Python for Data Science For Dummies written by John Paul Mueller and published by John Wiley & Sons. This book was released on 2015-07-07 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.

Download or read book Python Data Science Handbook written by Jake VanderPlas and published by "O'Reilly Media, Inc.". This book was released on 2016-11-21 with total page 743 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Python 3 and Data Visualization written by Oswald Campesato and published by Mercury Learning and Information. This book was released on 2023-10-03 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python 3 and Data Visualization offers readers a deep dive into the world of Python 3 programming and the art of data visualization. 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, seamlessly leading into the world of data visualization using prominent libraries such as Matplotlib. Chapter 6 immerses the reader in Seaborn's rich visualization tools, offering insights into datasets like Iris and Titanic. The appendix covers other visualization tools and techniques, including SVG graphics, D3 for dynamic visualizations, and more. The book also includes companion files with numerous Python code samples and figures. From foundational Python concepts to the intricacies of data visualization, this book serves as a comprehensive resource for both beginners and seasoned professionals. FEATURES: Covers numerous tools for mastering visualization including NumPy, Pandas, SQL, Matplotlib, and Seaborn Includes an introductory chapter on Python 3 basics Features companion files with numerous Python code samples and figures

Download or read book Data Science Fundamentals for Python and MongoDB written by David Paper and published by Apress. This book was released on 2018-05-10 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build the foundational data science skills necessary to work with and better understand complex data science algorithms. This example-driven book provides complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience. Coding examples include visualizations whenever appropriate. The book is a necessary precursor to applying and implementing machine learning algorithms. The book is self-contained. All of the math, statistics, stochastic, and programming skills required to master the content are covered. In-depth knowledge of object-oriented programming isn’t required because complete examples are provided and explained. Data Science Fundamentals with Python and MongoDB is an excellent starting point for those interested in pursuing a career in data science. Like any science, the fundamentals of data science are a prerequisite to competency. Without proficiency in mathematics, statistics, data manipulation, and coding, the path to success is “rocky” at best. The coding examples in this book are concise, accurate, and complete, and perfectly complement the data science concepts introduced. What You'll Learn Prepare for a career in data science Work with complex data structures in Python Simulate with Monte Carlo and Stochastic algorithms Apply linear algebra using vectors and matrices Utilize complex algorithms such as gradient descent and principal component analysis Wrangle, cleanse, visualize, and problem solve with data Use MongoDB and JSON to work with data Who This Book Is For The novice yearning to break into the data science world, and the enthusiast looking to enrich, deepen, and develop data science skills through mastering the underlying fundamentals that are sometimes skipped over in the rush to be productive. Some knowledge of object-oriented programming will make learning easier.

Download or read book Ultimate Python Libraries for Data Analysis and Visualization written by Abhinaba Banerjee and published by Orange Education Pvt Ltd. This book was released on 2024-04-04 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: Test your Data Analysis skills to its fullest using Python and other no-code tools KEY FEATURES ● Comprehensive coverage of Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, Julius AI for data acquisition, preparation, analysis, and visualization ● Real-world projects and practical applications for hands-on learning ● In-depth exploration of low-code and no-code tools for enhanced productivity DESCRIPTION Ultimate Data Analysis and Visualization with Python is your comprehensive guide to mastering the intricacies of data analysis and visualization using Python. This book serves as your roadmap to unlocking the full potential of Python for extracting insights from data using Pandas, NumPy, Matplotlib, Seaborn, and Julius AI. Starting with the fundamentals of data acquisition, you'll learn essential techniques for gathering and preparing data for analysis. From there, you’ll dive into exploratory data analysis, uncovering patterns and relationships hidden within your datasets. Through step-by-step tutorials, you'll gain proficiency in statistical analysis, time series forecasting, and signal processing, equipping you with the tools to extract actionable insights from any dataset. What sets this book apart is its emphasis on real-world applications. With a series of hands-on projects, you’ll apply your newfound skills to analyze diverse datasets spanning industries such as finance, healthcare, e-commerce, and more. By the end of the book, you'll have the confidence and expertise to tackle any data analysis challenge with Python. To aid your journey, the book includes a handy Python cheat sheet in the appendix, serving as a quick reference guide for common functions and syntax. WHAT WILL YOU LEARN ● Acquire data from various sources using Python, including web scraping, APIs, and databases. ● Clean and prepare datasets for analysis, handling missing values, outliers, and inconsistencies. ● Conduct exploratory data analysis to uncover patterns, trends, and relationships within your data. ● Perform statistical analysis using Python libraries such as NumPy and Pandas, including hypothesis testing and regression analysis. ● Master time series analysis techniques for forecasting future trends and making data-driven decisions. ● Apply signal processing methods to analyze and interpret signals in data, such as audio, image, and sensor data. ● Engage in real-world projects across diverse industries, from finance to healthcare, to reinforce your skills and experience. ● Utilize Python for in-depth analysis of real-world datasets, gaining practical experience and insights. ● Refer to the Python cheat sheet in the appendix for quick access to common functions and syntax, aiding your learning and development. WHO IS THIS BOOK FOR? This book is ideal for beginners, professionals, or students aiming to enhance their careers through hands-on experience in data acquisition, preparation, analysis, time series, and signal processing. Prerequisite knowledge includes basic Python and introductory statistics. Whether starting fresh or seeking to refresh skills, this comprehensive guide helps readers upskill effectively. TABLE OF CONTENTS 1. Introduction to Data Analysis and Data Visualization using Python 2. Data Acquisition 3. Data Cleaning and Preparation 4. Exploratory Data Analysis 5. Statistical Analysis 6. Time Series Analysis and Forecasting 7. Signal Processing 8. Analyzing Real-World Data Sets using Python APPENDIX A Python Cheat Sheet Index

Download or read book Python Data Analytics and Visualization written by Phuong Vo.T.H and published by Packt Publishing Ltd. This book was released on 2017-03-31 with total page 866 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand, evaluate, and visualize data About This Book Learn basic steps of data analysis and how to use Python and its packages A step-by-step guide to predictive modeling including tips, tricks, and best practices Effectively visualize a broad set of analyzed data and generate effective results Who This Book Is For This book is for Python Developers who are keen to get into data analysis and wish to visualize their analyzed data in a more efficient and insightful manner. What You Will Learn Get acquainted with NumPy and use arrays and array-oriented computing in data analysis Process and analyze data using the time-series capabilities of Pandas Understand the statistical and mathematical concepts behind predictive analytics algorithms Data visualization with Matplotlib Interactive plotting with NumPy, Scipy, and MKL functions Build financial models using Monte-Carlo simulations Create directed graphs and multi-graphs Advanced visualization with D3 In Detail You will start the course with an introduction to the principles of data analysis and supported libraries, along with NumPy basics for statistics and data processing. Next, you will overview the Pandas package and use its powerful features to solve data-processing problems. Moving on, you will get a brief overview of the Matplotlib API .Next, you will learn to manipulate time and data structures, and load and store data in a file or database using Python packages. You will learn how to apply powerful packages in Python to process raw data into pure and helpful data using examples. You will also get a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or building helpful products such as recommendations and predictions using Scikit-learn. After this, you will move on to a data analytics specialization—predictive analytics. Social media and IOT have resulted in an avalanche of data. You will get started with predictive analytics using Python. You will see how to create predictive models from data. You will get balanced information on statistical and mathematical concepts, and implement them in Python using libraries such as Pandas, scikit-learn, and NumPy. You'll learn more about the best predictive modeling algorithms such as Linear Regression, Decision Tree, and Logistic Regression. Finally, you will master best practices in predictive modeling. After this, you will get all the practical guidance you need to help you on the journey to effective data visualization. Starting with a chapter on data frameworks, which explains the transformation of data into information and eventually knowledge, this path subsequently cover the complete visualization process using the most popular Python libraries with working examples This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Getting Started with Python Data Analysis, Phuong Vo.T.H &Martin Czygan Learning Predictive Analytics with Python, Ashish Kumar Mastering Python Data Visualization, Kirthi Raman Style and approach The course acts as a step-by-step guide to get you familiar with data analysis and the libraries supported by Python with the help of real-world examples and datasets. It also helps you gain practical insights into predictive modeling by implementing predictive-analytics algorithms on public datasets with Python. The course offers a wealth of practical guidance to help you on this journey to data visualization

Download or read book Data Analysis and Visualization Using Python written by Dr. Ossama Embarak and published by Apress. This book was released on 2018-11-20 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Python, you will take a closer look at the Python environment and get acquainted with editors such as Jupyter Notebook and Spyder. After going through a primer on Python programming, you will grasp fundamental Python programming techniques used in data science. Moving on to data visualization, you will see how it caters to modern business needs and forms a key factor in decision-making. You will also take a look at some popular data visualization libraries in Python. Shifting focus to data structures, you will learn the various aspects of data structures from a data science perspective. You will then work with file I/O and regular expressions in Python, followed by gathering and cleaning data. Moving on to exploring and analyzing data, you will look at advanced data structures in Python. Then, you will take a deep dive into data visualization techniques, going through a number of plotting systems in Python. In conclusion, you will complete a detailed case study, where you’ll get a chance to revisit the concepts you’ve covered so far. What You Will LearnUse Python programming techniques for data science Master data collections in Python Create engaging visualizations for BI systems Deploy effective strategies for gathering and cleaning data Integrate the Seaborn and Matplotlib plotting systems Who This Book Is For Developers with basic Python programming knowledge looking to adopt key strategies for data analysis and visualizations using Python.

Download or read book Data Visualization with Python for Beginners Visualize Your Data Using Pandas Matplotlib and Seaborn written by Ai Publishing and published by AI Publishing LLC. This book was released on 2020-02-14 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Visualization using Python for Beginners Are you looking for a hands-on approach to learn Python for Data Visualization Fast? Do you need to start learning Python for Data Visualization from Scratch? This book is for you. This book works as guide to present fundamental Python Libraries and basis related to Data Visualization using Python. Data science and data visualization are two different but interrelated concepts. Data science refers to the science of extracting and exploring data in order to find patterns that can be used for decision making at different levels. Data visualization can be considered as a subdomain of data science where you visualize data with the help of graphs and tables in order to find out which data is most significant and can help in the identification of important patterns. This book is dedicated to data visualization and explains how to perform data visualization on a variety of datasets using various data visualization libraries written in the Python programming language. It is suggested that you use this book for data visualization purposes only and not for decision making. For decision making and pattern identification, read this book in conjunction with a dedicated book on machine learning and data science. We will start by digging into Python programming as all the projects are developed using it, and it is currently the most used programming language in the world. We will also explore the most-famous libraries for Data Visualization such as Pandas, Numpy, Matplotlib, Seaborn, etc . What this book offers... You will learn all about python in three modules, one for Plotting with Matplotlib, one for Plotting with Seaborn, and a final one Pandas for Data Visualization. All three modules will contain hands-on projects using real-world datasets and a lot of exercises. Clear and Easy to Understand Solutions All solutions in this book are extensively tested by a group of beta readers. The solutions provided are simplified as much as possible so that they can serve as examples for you to refer to when you are learning a new skill. What this book aims to do... This book is written with one goal in mind - to help beginners overcome their initial obstacles to learning Data Visualization using Python. A lot of times, newbies tend to feel intimidated by coding and data. The goal of this book is to isolate the different concepts so that beginners can gradually gain competency in the fundamentals of Python before working on a project. Beginners in Python coding and Data Science does not have to be scary or frustrating when you take one step at a time. Ready to start practicing and visualizing your data using Python? Click the BUY button now to download this book Topics Covered: Basic Plotting with Matplotlib Advanced Plotting with Matplotlib Introduction to the Python Seaborn Library Advanced Plotting with Seaborn Introduction to Pandas Library for Data Analysis Pandas for Data Visualization 3D Plotting with Matplotlib Interactive Data Visualization with Bokeh Interactive Data Visualization with Plotly Hands-on Project Exercises Click the BUY button and download the book now to start learning and coding Python for Data Visualization. ** MONEY BACK GUARANTEE BY AMAZON ** If you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform or contact us by sending an email at [email protected]. **GET YOUR COPY NOW, the price will be 19.99$ soon**

Download or read book Hands on Data Analysis and Visualization with Pandas written by PURNA CHANDER RAO. KATHULA and published by BPB Publications. This book was released on 2020-08-13 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use JupyterLab, Numpy, pandas, Scipy, Matplotlib, and Seaborn for Data science KEY FEATURESÊÊ _ Get familiar with different inbuilt Data structures, Functional programming, and Datetime objects. _ Handling heavy Datasets to optimize the data types for memory management, reading files in chunks, dask, and modin pandas. _ Time-series analysis to find trends, seasonality, and cyclic components. _ Seaborn to build aesthetic plots with high-level interfaces and customized themes. _ Exploratory data analysis with real-time datasets to maximize the insights about data. DESCRIPTIONÊ The book will start with quick introductions to Python and its ecosystem libraries for data science such as JupyterLab, Numpy, Pandas, SciPy, Matplotlib, and Seaborn. This book will help in learning python data structures and essential concepts such as Functions, Lambdas, List comprehensions, Datetime objects, etc. required for data engineering. It also covers an in-depth understanding of Python data science packages where JupyterLab used as an IDE for writing, documenting, and executing the python code, Numpy used for computation of numerical operations, Pandas for cleaning and reorganizing the data, handling large datasets and merging the dataframes to get meaningful insights. You will go through the statistics to understand the relation between the variables using SciPy and building visualization charts using Matplotllib and Seaborn libraries. WHAT WILL YOU LEARNÊ _ Learn about Python data containers, their methods, and attributes. _ Learn Numpy arrays for the computation of numerical data. _ Learn Pandas data structures, DataFrames, and Series. _ Learn statistics measures of central tendency, central limit theorem, confidence intervals, and hypothesis testing. _ A brief understanding of visualization, control, and draw different inbuilt charts to extract important variables, detect outliers, and anomalies using Matplotlib and Seaborn. Ê WHO THIS BOOK IS FORÊ This book is for anyone who wants to use Python for Data Analysis and Visualization. This book is for novices as well as experienced readers with working knowledge of the pandas library. Basic knowledge of Python is a must.Ê TABLE OF CONTENTSÊ 1. Introduction to Data Analysis 2. Jupyter lab 3. Python overview 4. Introduction to Numpy 5. Introduction to PandasÊ 6. Data Analysis 7. Time-Series Analysis 8. Introduction to Statistics 9. Matplotlib 10. Seaborn 11. Exploratory Data Analysis

Download or read book Data Science Fundamentals and Practical Approaches written by Dr. Gypsy Nandi and published by BPB Publications. This book was released on 2020-06-02 with total page 587 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to process and analysis data using PythonÊ KEY FEATURESÊ - The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. - The book is not just dealing with the background mathematics alone or only the programs but beautifully correlates the background mathematics to the theory and then finally translating it into the programs. - A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. DESCRIPTION This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems.Ê Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic.Ê WHAT WILL YOU LEARNÊ Perform processing on data for making it ready for visual plot and understand the pattern in data over time. Understand what machine learning is and how learning can be incorporated into a program. Know how tools can be used to perform analysis on big data using python and other standard tools. Perform social media analytics, business analytics, and data analytics on any data of a company or organization. WHO THIS BOOK IS FOR The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. TABLE OF CONTENTS 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics

Download or read book Python for Data Analysis written by Wes McKinney and published by "O'Reilly Media, Inc.". This book was released on 2017-09-25 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples