Python Programming for Data Analysis (Record no. 78453)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 04590nam a22006015i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-030-68952-0 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220801220330.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 210504s2021 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783030689520 |
| -- | 978-3-030-68952-0 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 621.382 |
| 100 1# - AUTHOR NAME | |
| Author | Unpingco, José. |
| 245 10 - TITLE STATEMENT | |
| Title | Python Programming for Data Analysis |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2021. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XII, 263 p. 134 illus., 123 illus. in color. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- Basic Language -- Basic Data Structures -- Basic Programming -- File Input/Output -- Dealing with Errors -- Power Python Features to Master -- Advanced Language Features -- Using modules -- Object oriented programming -- Debugging from Python -- Using Numpy – Numerical Arrays in Python -- Data Visualization Using Python -- Bokeh for Web-based Visualization -- Getting Started with Pandas -- Some Useful Python-Fu -- Conclusion. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. 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. 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. To get the most out of this book, open a Python interpreter and type along with the many code samples. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-030-68952-0 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2021. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Telecommunication. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer science—Mathematics. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Mathematical statistics. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Quantitative research. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Signal processing. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Statistics . |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data mining. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Communications Engineering, Networks. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Probability and Statistics in Computer Science. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Analysis and Big Data. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Signal, Speech and Image Processing . |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Mining and Knowledge Discovery. |
| 912 ## - | |
| -- | ZDB-2-ENG |
| 912 ## - | |
| -- | ZDB-2-SXE |
No items available.