Getting Started with Python Data Analysis (notice n° 69636)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03697cam a2200289zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88853327 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250107225408.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250107s2015 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781785285110 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88853327 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FR-PaCSA |
| Language of cataloging | en |
| Transcribing agency | |
| Description conventions | rda |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Vo.t.h, Phuong |
| 245 01 - TITLE STATEMENT | |
| Title | Getting Started with Python Data Analysis |
| Statement of responsibility, etc. | ['Vo.t.h, Phuong', 'Czygan, Martin'] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | Packt Publishing |
| Date of production, publication, distribution, manufacture, or copyright notice | 2015 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| 336 ## - CONTENT TYPE | |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type code | c |
| Source | rdamdedia |
| 338 ## - CARRIER TYPE | |
| Carrier type code | c |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Learn to use powerful Python libraries for effective data processing and analysisAbout This BookLearn the basic processing steps in data analysis and how to use Python in this area through supported packages, especially Numpy, Pandas, and MatplotlibCreate, manipulate, and analyze your data to extract useful information to optimize your systemA hands-on guide to help you learn data analysis using PythonWho This Book Is ForIf you are a Python developer who wants to get started with data analysis and you need a quick introductory guide to the python data analysis libraries, then this book is for you.What You Will LearnUnderstand the importance of data analysis and get familiar with its processing stepsGet acquainted with Numpy to use with arrays and array-oriented computing in data analysisCreate effective visualizations to present your data using MatplotlibProcess and analyze data using the time series capabilities of PandasInteract with different kind of database systems, such as file, disk format, Mongo, and RedisApply the supported Python package to data analysis applications through examplesExplore predictive analytics and machine learning algorithms using Scikit-learn, a Python libraryIn DetailData analysis is the process of applying logical and analytical reasoning to study each component of data. Python is a multi-domain, high-level, programming language. It's often used as a scripting language because of its forgiving syntax and operability with a wide variety of different eco-systems. Python has powerful standard libraries or toolkits such as Pylearn2 and Hebel, which offers a fast, reliable, cross-platform environment for data analysis.With this book, we will get you started with Python data analysis and show you what its advantages are.The book starts by introducing the principles of data analysis and supported libraries, along with NumPy basics for statistic and data processing. Next it provides an overview of the Pandas package and uses its powerful features to solve data processing problems.Moving on, the book takes you through a brief overview of the Matplotlib API and some common plotting functions for DataFrame such as plot. Next, it will teach you to manipulate the time and data structure, and load and store data in a file or database using Python packages. The book will also teach you how to apply powerful packages in Python to process raw data into pure and helpful data using examples.Finally, the book gives you a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or build helpful products, such as recommendations and predictions using scikit-learn.Style and approachThis is an easy-to-follow, step-by-step guide to get you familiar with data analysis and the libraries supported by Python. Topics are explained with real-world examples wherever required. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Vo.t.h, Phuong |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Czygan, Martin |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88853327">https://international.scholarvox.com/netsen/book/88853327</a> |
| Electronic format type | text/html |
| Host name | |
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