Python programming for data analysis (Record no. 1148)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02478 a2200205 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250416165706.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240426b2021|||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783030689544 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | English |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 621.382 U67 |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Unpingco, Jose |
| Relator term | Author |
| 245 ## - TITLE STATEMENT | |
| Title | Python programming for data analysis |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher | Springer, |
| Year of publication | 2021. |
| Place of publication | Switzerland: |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xii, 263p.; 23cms. |
| 500 ## - GENERAL NOTE | |
| General note | This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns.<br/>After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.<br/><br/>The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis.<br/>To get the most out of this book, open a Python interpreter and type along with the many code samples. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Basic programming |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Object-oriented programming |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Visualizing data |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Permanent Location | Current Location | Shelving location | Full call number | Accession Number | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Dr. S. R. Ranganathan Library | Dr. S. R. Ranganathan Library | General Stacks | 621.382 U67 | 3173 | Books |