Python Data Science Handbook
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Python Data Science Handbook
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Author : Jake VanderPlas
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-11-21
Python Data Science Handbook written by Jake VanderPlas and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-21 with Computers categories.
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
Python Data Science Handbook
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Author : Jake VanderPlas
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-12-06
Python Data Science Handbook written by Jake VanderPlas and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-06 with Computers categories.
Python is a first-class tool for many researchers, primarily 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 new edition of 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 the second edition of 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: IPython and Jupyter provide computational environments for scientists using Python NumPy includes the ndarray for efficient storage and manipulation of dense data arrays Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data Matplotlib includes capabilities for a flexible range of data visualizations Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms
Hands On Data Analysis With Pandas
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Author : Stefanie Molin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-04-29
Hands On Data Analysis With Pandas written by Stefanie Molin and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Computers categories.
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.
Python Data Science Handbook
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Author : Jacob T. Vanderplas
language : en
Publisher:
Release Date : 2016
Python Data Science Handbook written by Jacob T. Vanderplas and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Data mining categories.
"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."--Provided by publisher.
Python Data Science Handbook 2nd Edition
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Author : Jake VanderPlas
language : en
Publisher:
Release Date : 2022
Python Data Science Handbook 2nd Edition written by Jake VanderPlas and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Python is a first-class tool for many researchers, primarily 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 new edition of Python Data Science Handbook do you get them all--IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. In this second edition, 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: IPython and Jupyter provide computational environments for scientists using Python NumPy includes the ndarray for efficient storage and manipulation of dense data arrays Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data Matplotlib includes capabilities for a flexible range of data visualizations Scikit-Learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms.
Python Data Science Handbook
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Author : Oliver Soranson
language : en
Publisher: Independently Published
Release Date : 2019-11-08
Python Data Science Handbook written by Oliver Soranson and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-08 with categories.
This book in detail will deal with a lot of concepts in layman explanation. Data science is one of the pioneers and most fast-developing tools now to develop applications. A little to know in a better way of this guide will help you understand a lot of things for the data science application. Here in this book, we will deal with many concepts that are either directly or indirectly linked to data science. First, we will start with a brief explanation of the data. Data is one of the most important things today. We will classify the data many times and in the next, we will discuss in detail the applications that machine learning and data science that deals with. In the next section, we will have a topic that will discuss all basic concepts in very little time. We will also discuss an example model that helps us understand things vividly. Also, there are a lot of concepts like NumPy, Pandas that should be dealt with in detail. We will also discuss data operations in detail. These are important and easy to learn. In the next few chapters, we will discuss topics like privacy and ethics. Below we will in detail list the main topics that are included in the book. · Explanation of data and types of data · A lot of explanation about third party libraries · Introduction to python · Introduction to a lot of python basic concepts · Basics of machine learning · Basics of data science like data modeling · Understanding complex topics like NumPy and pandas · A lot of description about privacy and ethics Why are you waiting for? Go grab this book.
Python Data Science
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Author : Jessica Rizzo
language : en
Publisher:
Release Date : 2019-08-25
Python Data Science written by Jessica Rizzo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-25 with categories.
If you're tired of licensing third-party software for data analysis, Python Data Science will help you do it for yourself! Recently, more and more companies are learning that they need to make DATA-DRIVEN decisions. And with big data and data science on the rise, we now have more data than we know what to do with. In fact, without a doubt, you have already experienced data science in one way or another. Obviously, you are interacting with data science products every time you search for information on the web by using search engines such as Google, or asking for directions with your mobile phone. Data science is the science and technology focused on collecting raw data and processing it in an effective manner. It is the combination of concepts and methods that make it possible to give meaning and understandability to huge volumes of data. Data science has been the force behind resolving some of our most common daily tasks for several years. In nearly all of our daily work, we directly or indirectly work on storing and exchanging data. With the rapid development of technology, the need to store data effectively is also increasing. That's why it needs to be handled properly. Basically, data science unearths the hidden insights of raw-data and uses them for productive output. Python is often used in data science today because it is a mature programming language that has excellent properties for newbie programmers. Some of the most remarkable of these properties are its easy to read code, suppression of non-mandatory delimiters, dynamic typing, and dynamic memory usage. Python is an interpreted language, and it can be executed in the Python console without any need to compile to machine language. "Python Data Science" teaches a complete course of data science, including key topics like data integration, data mining, python etc. We will explore NumPy for numerical data, Pandas for data analysis, IPython, Scikit-learn and Tensorflow for machine learning and business. Each of the chapters in this book is devoted to one of the most interesting aspects of data analysis and processing. The following are some of the major topics covered in Python Data Science: Understanding Data Science Getting Started with Python for Data Scientists Descriptive statistics Data Analysis and Libraries NumPy Arrays and Vectorized Computation Data Analysis with Pandas Data Visualization Data Mining Classifying with Scikit-learn Estimators Giving Computers the Ability to Learn from Data Training Machine Learning Algorithms The Python ecosystem for data science discussed within Python Data Science includes SciPy, NumPy, Matplotlib, Pandas, and Scikit-learn, which provides all of the data science algorithms. Data processing and analysis is one of the hottest areas of IT, where developers who can handle projects of any level, from social networks to trained systems, are constantly required. We hope this book will be the starting point for your journey into the fascinating world of Data Science. To get started on your Python adventure, just scroll back up and click the 'Buy' button.
Python Programming
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Author : Oliver Soranson
language : en
Publisher: Independently Published
Release Date : 2020-03-30
Python Programming written by Oliver Soranson and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-30 with categories.
Discover the world of python with this incredible 3-in-1 book bundle! Do you want to uncover the secrets of python data analysis, data science, and machine learning? Looking for the best ways to familiarize yourself with this amazing language, no matter your level of experience? Then this is the bundle for you! Inside this incredible 3 book collection, you'll discover how you can get started with the world of python. Containing a wealth of practical strategies, tips and tricks, and easy-to-understand explanations, now it's never been easier to begin programming with the world of python. With a breakdown of data science, data analysis, and even the wonders of machine learning, this collection is perfect for anybody looking to begin their programming journey. Inside book one, you'll discover: An Exploration Of Machine Learning Must-Know Terminology, Notations, and Programming Jargon How To Use Conditional Statements How To Work With Pandas, Jupyter and Ipython Tips And Tricks For Creating Machine Learning Algorithms And More! Inside book two, you'll find: An Introduction To The World of Python Python Origins, History, And Function Breaking Down The Basics - Python Fundamentals You Need To Know Understanding Numpy Arrays, Operations, Objects, and The Data Preparing Data, Creating Statistics, and More And Real-Life Examples of Python In Action! And in book three, you'll learn: The Fundamentals of Data Science How Python Can Help You Manage Big Data Essential Strategies For Using Python In Data Science Acquiring, Examining, and Processing Data Exploring The Ethics and Privacy of Big Data And So Much More! So don't wait! It's time for you to discover why python is a favorite language for programmers the world over. Even if you're a complete beginner, or you've never used python before, this bundle explains everything you need to know in a simple, easy-to-follow way. Understand machine learning, discover how to analyze data, and begin your journey with python today! So what are you waiting for? Buy now to discover how to get started with python programming!
Python Data Science
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Author : Andrew Park
language : en
Publisher:
Release Date : 2020-11-14
Python Data Science written by Andrew Park and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-14 with Computers categories.
If you want to learn more about Data Science or how to master it with the Python Programming Language, then keep reading. Data Science is one of the biggest buzzwords in the business world nowadays. Many businesses know the importance of collecting information, but as they can collect so much data in a short period, the real question is: "what is the next step?" Data Science includes all the different steps that you take with the data: collecting and cleaning them if they come from more than one source, analyzing them, applying Machine Learning algorithms and models, and then presenting your findings from the analysis with some good Data Visualizations. And this is what you will learn in Python Data Science. You will learn about the main steps that are needed to correctly implement Data Science techniques and the algorithms to help you sort through the data and see some amazing results. Some of the topics that we will discuss inside include: What data science is all about and why so many companies are using it to give them a competitive edge. Why Python and how to use it to implement Data Science What is the intersection between Machine Learning and Data Science and how to combine them The main Data Structures & Object-Oriented Python, with practical codes and exercises to use Python Functions and Modules in Python The 7 most important algorithms and models in Data Science Data Aggregation and Group Operations 9 important Data Mining techniques in Data Science Interaction with databases and data in the cloud And Much More! Where most books only focus on how collecting and cleaning the data, this book goes further, providing guidance on how to perform a proper analysis in order to extract precious information that may be vital for a business. Don't miss the opportunity to learn more about these topics. Even if you have never implemented Data Science techniques, learning them is easier than it looks. You just need the right guidance. And Python Data Science provides all the knowledge you need in a simple and practical way. Regardless of your previous experience, you will learn, the techniques to manipulate and process datasets, the principles of Python programming, and its most important real-world applications. Would You Like To Know More? Scroll Up And Click The BUY NOW Button To Get Your Copy Now!
Data Science From Scratch With Python
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Author : Peters Morgan
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-07-03
Data Science From Scratch With Python written by Peters Morgan and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-03 with categories.
***** BUY NOW (Will soon return to 35.59) ******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of learning data science from scratch using Python? If you are looking for a complete step by step guide to data science using Python from scratch, this book is for you.After his great success with his first book "Data Analysis from Scratch with Python", Peters Morgan publish his second book focusing now in data science and machine learning. It is considered by practitioners as the easiest guide ever written in this domain. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. If you need to see the quality of our job, AI Sciences Company offering you a free eBook in Machine Learning with Python written by Alain Kaufmann at https://aisciences.lpages.co/ai-science-l1/.To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples The Book give complete instructions for manipulating, processing, cleaning, modeling and crunching datasets in Python. This is a hands-on guide with practical case studies of data analysis problems effectively. You will learn pandas, NumPy, IPython, and Jupiter in the Process. Target Users Target UsersThe most suitable users would include: Beginners who want to approach data science, but are too afraid of complex math to start Newbies in computer science techniques and data science Professionals in data science and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on data science What's Inside This Book? Part 1: Data Science Fundamentals, Concepts and Algorithms Introduction Statistics Probability Bayes' Theorem and Naïve Bayes Algorithm Asking the Right Question Data Acquisition Data Preparation Data Exploration Data Modelling Data Presentation Supervised Learning Algorithms Unsupervised Learning Algorithms Semi-supervised Learning Algorithms Reinforcement Learning Algorithms Overfitting and Underfitting Correctness The Bias-Variance Trade-off Feature Extraction and Selection Part 2: Data Science in Practice Overview of Python Programming Language Python Data Science Tools Jupyter Notebook Numerical Python (Numpy) Pandas Scientific Python (Scipy) Matplotlib Scikit-Learn K-Nearest Neighbors Naive Bayes Simple and Multiple Linear Regression Logistic Regression GLM models Decision Trees and Random forest Perceptrons Backpropagation Clustering Natural Language Processing Frequently Asked Questions Q: Is this book for me and do I need programming experience?A: if you want to smash Python for data science and machine learning, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK.Q: Can I have a refund if this book is not fitted for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected].