Python for Data Analysis (Record no. 295671)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02304nam a2200181Ia 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789351100065 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.133 MCK-P |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Wes McKinney |
| 245 ## - TITLE STATEMENT | |
| Title | Python for Data Analysis |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Beijing |
| Name of publisher, distributor, etc. | O'Reilly |
| Date of publication, distribution, etc. | 2013 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xiii, 452 p. : illustrations |
| 500 ## - GENERAL NOTE | |
| General note | Includes index. |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Preliminaries -- Introductory examples -- IPython : an interactive computing and development environment -- NumPy basics : arrays and vectorized computation -- Getting started with pandas -- Data loading, storage, and file formats -- Data wrangling : clean, transform, merge, reshape -- Plotting and visualization -- Data aggregation and group operations -- Time series -- Financial and economic data applications -- Advancded NumPy. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Python (Computer program language) Programming languages (Electronic computers) Data mining. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Book |
| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Date last checked out | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dept. of Computational Biology and Bioinformatics | Dept. of Computational Biology and Bioinformatics | Processing Center | 01/09/2015 | 3 | 005.133 MCK-P | DCB2508 | 12/09/2023 | 26/04/2023 | 19/07/2019 | Book |