MARC details
| 000 -LEADER |
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| fixed length control field |
03415nam a22002177a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20211020165438.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
190903b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9789352136414 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
005.133 |
| Item number |
MCK |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
McKinney, Wes |
| 245 ## - TITLE STATEMENT |
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| Title |
Python for data analysis: data wrangling with pandas |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc. |
O'Reilly Media |
| Place of publication, distribution, etc. |
Sebastopol |
| Date of publication, distribution, etc. |
2013 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiii, 447 p. |
| 365 ## - TRADE PRICE |
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| Price type code |
INR |
| Price amount |
1450.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Table of Contents<br/><br/>1.Preliminaries<br/><br/>2.Python Language Basics, IPython, and Jupyter Notebooks<br/><br/>3.Built-in Data Structures, Functions, and Files<br/><br/>4. NumPy Basics: Arrays and Vectorized Computation<br/><br/>5.Getting Started with pandas<br/><br/>6.Data Loading, Storage, and File Formats<br/><br/>7. Data Cleaning and Preparation<br/><br/>8. Data Wrangling: Join, Combine, and Reshape<br/><br/>9. Plotting and Visualization<br/><br/>10.Data Aggregation and Group Operations<br/><br/>11.Time Series<br/><br/>12. Advanced pandas<br/><br/>13. Introduction to Modeling Libraries in Python<br/><br/>14. Data Analysis Examples<br/><br/> |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. YouÂll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. ItÂs ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.Use the IPython shell and Jupyter notebook for exploratory computingLearn basic and advanced features in NumPy (Numerical Python)Get started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples<br/><br/>About the Author<br/><br/>Wes McKinney<br/><br/>Wes McKinney is a New York−based software developer and entrepreneur. After finishing his undergraduate degree in mathematics at MIT in 2007, he went on to do quantitative finance work at AQR Capital Management in Greenwich, CT. Frustrated by cumbersome data analysis tools, he learned Python and started building what would later become the pandas project. He's now an active member of the Python data community and is an advocate for the use of Python in data analysis, finance, and statistical computing applications.<br/><br/>Wes was later the co-founder and CEO of DataPad, whose technology assets and team were acquired by Cloudera in 2014. He has since become involved in big data technology, joining the Project Management Committees for the Apache Arrow and Apache Parquet projects in the Apache Software Foundation. In 2016, he joined Two Sigma Investments in New York City, where he continues working to make data analysis faster and easier through open source software. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Python (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Data mining |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Programming languages (Electronic computers) |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |