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    Python for Data Analysis (Paperback) By (author) Wes McKinney

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    DescriptionFinding 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


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    Title
    Python for Data Analysis
    Authors and contributors
    By (author) Wes McKinney
    Physical properties
    Format: Paperback
    Number of pages: 472
    Width: 175 mm
    Height: 231 mm
    Thickness: 28 mm
    Weight: 794 g
    Language
    English
    ISBN
    ISBN 13: 9781449319793
    ISBN 10: 1449319793
    Classifications

    BIC E4L: COM
    Nielsen BookScan Product Class 3: S10.2
    B&T Book Type: NF
    B&T Modifier: Region of Publication: 01
    B&T Modifier: Subject Development: 20
    B&T Modifier: Academic Level: 05
    B&T Modifier: Text Format: 01
    B&T General Subject: 227
    DC22: 005.13/3, 005.133
    B&T Merchandise Category: COM
    LC subject heading:
    Ingram Subject Code: XL
    BIC subject category V2: UMW
    Warengruppen-Systematik des deutschen Buchhandels: 16330
    LC subject heading: , ,
    BISAC V2.8: COM018000
    B&T Approval Code: A93603256, A93663600
    BISAC V2.8: COM051360
    DC23: 006.76
    LC classification: QA76.73.P98 M42 2013
    Thema V1.0: UNC, UMW
    Publisher
    O'Reilly Media, Inc, USA
    Imprint name
    O'Reilly Media, Inc, USA
    Publication date
    01 November 2012
    Publication City/Country
    Sebastopol
    Author Information
    Wes McKinney is the main author of pandas, the popular open source Python library for data analysis. Wes is an active speaker and participant in the Python and open source communities. He worked as a quantitative analyst at AQR Capital Management before founding an enterprise data analysis company, Lambda Foundry, in 2012. He graduated from MIT with an S.B. in Mathematics.