Download or read book Biological Data Exploration with Python Pandas and Seaborn written by Martin Jones and published by . This book was released on 2020-06-03 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: In biological research, we''re currently in a golden age of data. It''s never been easier to assemble large datasets to probe biological questions. But these large datasets come with their own problems. How to clean and validate data? How to combine datasets from multiple sources? And how to look for patterns in large, complex datasets and display your findings? The solution to these problems comes in the form of Python''s scientific software stack. The combination of a friendly, expressive language and high quality packages makes a fantastic set of tools for data exploration. But the packages themselves can be hard to get to grips with. It''s difficult to know where to get started, or which sets of tools will be most useful. Learning to use Python effectively for data exploration is a superpower that you can learn. With a basic knowledge of Python, pandas (for data manipulation) and seaborn (for data visualization) you''ll be able to understand complex datasets quickly and mine them for biological insight. You''ll be able to make beautiful, informative charts for posters, papers and presentations, and rapidly update them to reflect new data or test new hypotheses. You''ll be able to quickly make sense of datasets from other projects and publications - millions of rows of data will no longer be a scary prospect! In this book, Dr. Jones draws on years of teaching experience to give you the tools you need to answer your research questions. Starting with the basics, you''ll learn how to use Python, pandas, seaborn and matplotlib effectively using biological examples throughout. Rather than overwhelm you with information, the book concentrates on the tools most useful for biological data. Full color illustrations show hundreds of examples covering dozens of different chart types, with complete code samples that you can tweak and use for your own work. This book will help you get over the most common obstacles when getting started with data exploration in Python. You''ll learn about pandas'' data model; how to deal with errors in input files and how to fit large datasets in memory. The chapters on visualization will show you how to make sophisticated charts with minimal code; how to best use color to make clear charts, and how to deal with visualization problems involving large numbers of data points. Chapters include: Getting data into pandas: series and dataframes, CSV and Excel files, missing data, renaming columns Working with series: descriptive statistics, string methods, indexing and broadcasting Filtering and selecting: boolean masks, selecting in a list, complex conditions, aggregation Plotting distributions: histograms, scatterplots, custom columns, using size and color Special scatter plots: using alpha, hexbin plots, regressions, pairwise plots Conditioning on categories: using color, size and marker, small multiples Categorical axes:strip/swarm plots, box and violin plots, bar plots and line charts Styling figures: aspect, labels, styles and contexts, plotting keywords Working with color: choosing palettes, redundancy, highlighting categories Working with groups: groupby, types of categories, filtering and transforming Binning data: creating categories, quantiles, reindexing Long and wide form: tidying input datasets, making summaries, pivoting data Matrix charts: summary tables, heatmaps, scales and normalization, clustering Complex data files: cleaning data, merging and concatenating, reducing memory FacetGrids: laying out multiple charts, custom charts, multiple heat maps Unexpected behaviours: bugs and missing groups, fixing odd scales High performance pandas: vectorization, timing and sampling Further reading: dates and times, alternative syntax

Download or read book Proteomics for Biological Discovery written by Timothy D. Veenstra and published by John Wiley & Sons. This book was released on 2006-06-12 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by recognized experts in the study of proteins, Proteomics for Biological Discovery begins by discussing the emergence of proteomics from genome sequencing projects and a summary of potential answers to be gained from proteome-level research. The tools of proteomics, from conventional to novel techniques, are then dealt with in terms of underlying concepts, limitations and future directions. An invaluable source of information, this title also provides a thorough overview of the current developments in post-translational modification studies, structural proteomics, biochemical proteomics, microfabrication, applied proteomics, and bioinformatics relevant to proteomics. Presents a comprehensive and coherent review of the major issues faced in terms of technology development, bioinformatics, strategic approaches, and applications Chapters offer a rigorous overview with summary of limitations, emerging approaches, questions, and realistic future industry and basic science applications Discusses higher level integrative aspects, including technical challenges and applications for drug discovery Accessible to the novice while providing experienced investigators essential information Proteomics for Biological Discovery is an essential resource for students, postdoctoral fellows, and researchers across all fields of biomedical research, including biochemistry, protein chemistry, molecular genetics, cell/developmental biology, and bioinformatics.

Download or read book Python for Biologists written by Martin Jones and published by Createspace Independent Publishing Platform. This book was released on 2013 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python for biologists is a complete programming course for beginners that will give you the skills you need to tackle common biological and bioinformatics problems.

Download or read book Advanced Python for Biologists written by Martin O. Jones and published by Createspace Independent Publishing Platform. This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Python for Biologists is a programming course for workers in biology and bioinformatics who want to develop their programming skills. It starts with the basic Python knowledge outlined in Python for Biologists and introduces advanced Python tools and techniques with biological examples. You'll learn: - How to use object-oriented programming to model biological entities - How to write more robust code and programs by using Python's exception system - How to test your code using the unit testing framework - How to transform data using Python's comprehensions - How to write flexible functions and applications using functional programming - How to use Python's iteration framework to extend your own object and functions Advanced Python for Biologists is written with an emphasis on practical problem-solving and uses everyday biological examples throughout. Each section contains exercises along with solutions and detailed discussion.

Download or read book Pandas for Everyone written by Daniel Y. Chen and published by Addison-Wesley Professional. This book was released on 2017-12-15 with total page 1093 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning

Download or read book Parallel Algorithms for Regular Architectures written by Russ Miller and published by MIT Press. This book was released on 1996 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parallel-Algorithms for Regular Architectures is the first book to concentrate exclusively on algorithms and paradigms for programming parallel computers such as the hypercube, mesh, pyramid, and mesh-of-trees.

Download or read book Managing Your Biological Data with Python written by Allegra Via and published by CRC Press. This book was released on 2014-03-18 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take Control of Your Data and Use Python with ConfidenceRequiring no prior programming experience, Managing Your Biological Data with Python empowers biologists and other life scientists to work with biological data on their own using the Python language. The book teaches them not only how to program but also how to manage their data. It shows how

Download or read book Introduction to Data Science written by Laura Igual and published by Springer. This book was released on 2017-02-22 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.

Download or read book Designing Efficient Algorithms for Parallel Computers written by Michael Jay Quinn and published by McGraw-Hill Companies. This book was released on 1987 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematics of Computing -- Parallelism.

Download or read book Modern Python Bio Informatics written by Dr. Amarendra Alluri and published by RK Publication. This book was released on 2024-09-20 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern Python Bioinformatics is an insightful guide merging Python programming with bioinformatics, designed for both beginners and seasoned professionals in computational biology. This book covers essential Python skills and advanced bioinformatics concepts, including DNA/RNA sequencing, protein structure analysis, and data visualization. It emphasizes practical applications with examples and projects that demonstrate how to handle biological data, perform statistical analyses, and develop efficient bioinformatics workflows. With accessible explanations and code snippets, it equips readers to tackle real-world challenges in bioinformatics research and development.

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

Download or read book Mastering Python Data Visualization written by Kirthi Raman and published by Packt Publishing Ltd. This book was released on 2015-10-27 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generate effective results in a variety of visually appealing charts using the plotting packages in Python About This Book Explore various tools and their strengths while building meaningful representations that can make it easier to understand data Packed with computational methods and algorithms in diverse fields of science Written in an easy-to-follow categorical style, this book discusses some niche techniques that will make your code easier to work with and reuse Who This Book Is For If you are a Python developer who performs data visualization and wants to develop existing knowledge about Python to build analytical results and produce some amazing visual display, then this book is for you. A basic knowledge level and understanding of Python libraries is assumed. What You Will Learn Gather, cleanse, access, and map data to a visual framework Recognize which visualization method is applicable and learn best practices for data visualization Get acquainted with reader-driven narratives and author-driven narratives and the principles of perception Understand why Python is an effective tool to be used for numerical computation much like MATLAB, and explore some interesting data structures that come with it Explore with various visualization choices how Python can be very useful in computation in the field of finance and statistics Get to know why Python is the second choice after Java, and is used frequently in the field of machine learning Compare Python with other visualization approaches using Julia and a JavaScript-based framework such as D3.js Discover how Python can be used in conjunction with NoSQL such as Hive to produce results efficiently in a distributed environment In Detail Python has a handful of open source libraries for numerical computations involving optimization, linear algebra, integration, interpolation, and other special functions using array objects, machine learning, data mining, and plotting. Pandas have a productive environment for data analysis. These libraries have a specific purpose and play an important role in the research into diverse domains including economics, finance, biological sciences, social science, health care, and many more. The variety of tools and approaches available within Python community is stunning, and can bolster and enhance visual story experiences. This book offers practical guidance to help you on the journey to effective data visualization. Commencing with a chapter on the data framework, which explains the transformation of data into information and eventually knowledge, this book subsequently covers the complete visualization process using the most popular Python libraries with working examples. You will learn the usage of Numpy, Scipy, IPython, MatPlotLib, Pandas, Patsy, and Scikit-Learn with a focus on generating results that can be visualized in many different ways. Further chapters are aimed at not only showing advanced techniques such as interactive plotting; numerical, graphical linear, and non-linear regression; clustering and classification, but also in helping you understand the aesthetics and best practices of data visualization. The book concludes with interesting examples such as social networks, directed graph examples in real-life, data structures appropriate for these problems, and network analysis. By the end of this book, you will be able to effectively solve a broad set of data analysis problems. Style and approach The approach of this book is not step by step, but rather categorical. The categories are based on fields such as bioinformatics, statistical and machine learning, financial computation, and linear algebra. This approach is beneficial for the community in many different fields of work and also helps you learn how one approach can make sense across many fields

Download or read book Hands On Exploratory Data Analysis with Python written by Suresh Kumar Mukhiya and published by Packt Publishing Ltd. This book was released on 2020-03-27 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas Key FeaturesUnderstand the fundamental concepts of exploratory data analysis using PythonFind missing values in your data and identify the correlation between different variablesPractice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python packageBook Description Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes. What you will learnImport, clean, and explore data to perform preliminary analysis using powerful Python packagesIdentify and transform erroneous data using different data wrangling techniquesExplore the use of multiple regression to describe non-linear relationshipsDiscover hypothesis testing and explore techniques of time-series analysisUnderstand and interpret results obtained from graphical analysisBuild, train, and optimize predictive models to estimate resultsPerform complex EDA techniques on open source datasetsWho this book is for This EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. Fundamental knowledge of Python programming and statistical concepts is all you need to get started with this book.

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 The Bitcoin Ecosystem written by Hk Brar and published by Blurb. This book was released on 2019-05-23 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bitcoin is a powerfully magnificent ecosystem that decouples the concepts of trust and value from the need to have a centralized trusted party to facilitate trust-exchanges and value-exchanges. With Bitcoin, trust can be truly manufactured for the first time in transactions of financial consequence. In many ways, the Bitcoin paradigm is in line with today's marketplace shifts: the shifting of centralized management to self-management in the workplace, the rewiring of markets to bring buyer and seller interactions closer together rather than relying on centralized intermediaries, and the proliferation of platform-based business strategies that use supply-side economies of scale to open up businesses from the inside out (i.e. Uber and AirBnB). Bitcoin is a foundational disruptive technology. It lays the foundations for disruptive innovations in concert with the use of blockchain structures. In many respects, Bitcoin and the rise of all cryptocurrencies in the modern mainstream encourages broader embrace of blockchain-enabled digital transformations to occur across both businesses and industries such as education, energy, healthcare, telecommunications, transportation, etc. While Bitcoin was the first widespread use of a blockchain construct in the global domain, there are numerous inherent advantages of blockchain ecosystems. Bitcoin leads the pioneering charge in both the concepts of the Trust Economy and the Internet of Value. It will also be interesting to see the various intersections and junctures between the world of cryptocurrencies within the frame of current digital social experiences. 302 Pages, Full-Color, Gloss Cover, Paperback

Download or read book Effective Python Development for Biologists written by Martin Jones and published by Createspace Independent Publishing Platform. This book was released on 2016-09-26 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python is rapidly becoming the standard language for many talks in scientific research, and is particularly popular in biology and bioinformatics. One of the great strengths of Python is the ecosystem of tools and libraries that have grown up around it. This book introduces the novice biologist programmer to tools and techniques that make developing Python code easier and faster and will help you to write more reliable, performant programs. Written by a biologist, it focusses on solving the problems that students and researchers encounter every day: How do I make my program run faster? How can I be sure that my results are correct? How do I share this program with my colleagues? How can I speed up the process of writing my code? Chapters include: Environments for development - learn how you can take advantage of different tools for actually writing code, including those designed specifically for scientific work. Organising and sharing code - learn how Python's module and packaging system works, how to effectively reuse code across multiple projects, and how to share your programs with colleagues and the wider world. Testing - learn how automated testing can make your code more reliable, how to catch bugs before they impact your work, and how to edit code with confidence. Performance - learn how to make your code run quickly even on large datasets, how to understand the scaling behaviour of your code, and explore the trade offs involved in designing code. User interfaces - learn how to make your code more user friendly, how to design effective interfaces, and how to automate record-keeping with Python's logging system. About the author Martin started his programming career by learning Perl during the course of his PhD in evolutionary biology, and started teaching other people to program soon after. Since then he has taught introductory programming to hundreds of biologists, from undergraduates to PIs, and has maintained a philosophy that programming courses must be friendly, approachable, and practical. In his academic career, Martin mixed research and teaching at the University of Edinburgh, culminating in a two year stint as Lecturer in Bioinformatics. He now runs programming courses for biological researchers as a full time freelancer. Praise for Martin's previous books "Great, great book. I think this is the perfect book for any biologist to who wants to start learning to code with Python... I didn't know a command-line from a hole in the ground when I first opened up this book, and mere days later I was impressing my colleagues with my own DNA analysis programs." "Zero to writing useful programs in a weekend... Python for Biologists arrived last Thursday, 6/16/16, I spent the whole weekend glued to my laptop in a 2 1/2 day frenzy of coding, and I just finished it -- and came on Amazon to order the next one!" "One of the BEST coding books I've used in a long time. Direct applications in bioinformatics. I bought the advanced python book too." "The most useful guide to Python I've found...I've tried a few Python books, and this is by far the best for me."

Download or read book Hands On Data Analysis with Pandas written by Stefanie Molin and published by Packt Publishing Ltd. This book was released on 2021-04-29 with total page 788 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with pandas by working with real datasets and master data discovery, data manipulation, data preparation, and handling data for analytical tasks Key Features Perform efficient data analysis and manipulation tasks using pandas 1.x Apply pandas to different real-world domains with the help of step-by-step examples Make the most of pandas as an effective data exploration tool Book DescriptionExtracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.What you will learn Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Solve common data representation and analysis problems using pandas Build Python scripts, modules, and packages for reusable analysis code Who this book is for This book is for data science beginners, data analysts, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. Data scientists looking to implement pandas in their machine learning workflow will also find plenty of valuable know-how as they progress. You’ll find it easier to follow along with this book if you have a working knowledge of the Python programming language, but a Python crash-course tutorial is provided in the code bundle for anyone who needs a refresher.