About this product

Product Identifiers

PublisherO'reilly Media, Incorporated

ISBN-101449319793

ISBN-139781449319793

eBay Product ID (ePID)117281102

Product Key Features

Number of Pages463 Pages

Publication NamePython for Data Analysis : Data Wrangling with Pandas, Numpy, and Ipython

LanguageEnglish

Publication Year2012

SubjectGeneral, Data Processing, Databases / Data Mining, Programming Languages / Python

TypeTextbook

Subject AreaComputers

AuthorWes Mckinney

FormatTrade Paperback

Dimensions

Item Height0.9 in

Item Weight27.4 Oz

Item Length9.2 in

Item Width7 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN2012-538094

Dewey Edition23

IllustratedYes

Dewey Decimal005.133

Table Of ContentPreface Chapter 1: Preliminaries Chapter 2: Introductory Examples Chapter 3: IPython: An Interactive Computing and Development Environment Chapter 4: NumPy Basics: Arrays and Vectorized Computation Chapter 5: Getting Started with pandas Chapter 6: Data Loading, Storage, and File Formats Chapter 7: Data Wrangling: Clean, Transform, Merge, Reshape Chapter 8: Plotting and Visualization Chapter 9: Data Aggregation and Group Operations Chapter 10: Time Series Chapter 11: Financial and Economic Data Applications Chapter 12: Advanced NumPy Python Language Essentials Colophon

SynopsisDespite the explosive growth of data in industry after industry, learning and accessing data analysis tools has remained a challenge. This pragmatic guide demonstrates the nuts and bolts of manipulating, processing, cleaning, and crunching data with Python., Finding great data analysts is difficult. Despite the explosive growth of data in industries ranging from manufacturing and retail to high technology, finance, and healthcare, learning and accessing data analysis tools has remained a challenge. This pragmatic guide will help train you in one of the most important tools in the field - Python. Filled with practical case studies, Python for Data Analysis demonstrates the nuts and bolts of manipulating, processing, cleaning, and crunching data with Python. It also serves as a modern introduction to scientific computing in Python for data-intensive applications. Learn about the growing field of data analysis from an expert in the community. Learn everything you need to start doing real data analysis work with Python Get the most complete instruction on the basics of the "modern scientific Python platform" Learn from an insider who builds tools for the scientific stack Get an excellent introduction for novices and a wealth of advanced methods for experienced analysts, Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language. Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing. Use the IPython interactive shell as your primary development environment Learn basic and advanced NumPy (Numerical Python) features Get started with data analysis tools in the pandas library Use high-performance tools to load, clean, transform, merge, and reshape data Create scatter plots and static or interactive visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Measure data by points in time, whether it's specific instances, fixed periods, or intervals Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

LC Classification NumberQA76.9.D343

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by sckar6267
Apr 01, 2024
Well written and easy to follow
Great book to supplement my course work
Verified purchase: YesCondition: Pre-owned
by diga4964
Apr 21, 2024
Better condition than expected
Better condition than expected
Verified purchase: YesCondition: Pre-owned