Overview
- A concise guide to common Python features and popular data mining tools including pandas, SciPy, NumPy, and Matplotlib
- Quick reference format offers readers essential information and brief explanations with many examples
- Includes scikit-learn and core machine learning concepts
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Table of contents (11 chapters)
Keywords
About this book
​Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis.
Python for Data Mining Quick Syntax Reference covers each concept concisely, with many illustrative examples. You'll be introduced to several data mining packages, with examples of how to use each of them.
The first part covers core Python including objects, lists, functions, modules, and error handling. The second part covers Python's most important data mining packages: NumPy and SciPy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts, and scikitlearn for machine learning.
What You'll Learn
Programmers new to Python's data mining packages or with experience in other languages, who want a quick guide to Pythonic tools and techniques.
Python for Data Mining Quick Syntax Reference covers each concept concisely, with many illustrative examples. You'll be introduced to several data mining packages, with examples of how to use each of them.
The first part covers core Python including objects, lists, functions, modules, and error handling. The second part covers Python's most important data mining packages: NumPy and SciPy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts, and scikitlearn for machine learning.
What You'll Learn
- Install Python and choose a development environment
- Understand the basic concepts of object-oriented programming
- Import, open, and edit files
- Review the differences between Python 2.x and 3.x
Programmers new to Python's data mining packages or with experience in other languages, who want a quick guide to Pythonic tools and techniques.
Authors and Affiliations
About the author
Valentina Porcu is a computer geek with a passion for data mining and research, and a Ph.D in communication and complex systems. She has years of experience in teaching in universities in Italy, France and Morocco, and online, of course! She works as consultant in the field of data mining and machine learning and enjoys writing about new technologies and data mining. She spent the last 9 years working as freelancer and researcher in the field of social media analysis, benchmark analysis and web scraping for database building, in particular in the field of buzz analysis and sentiment analysis for universities, startups and web agencies across UK, France, US and Italy. Valentina is the founder of Datawiring, a popular Italian data science resource.
Bibliographic Information
Book Title: Python for Data Mining Quick Syntax Reference
Authors: Valentina Porcu
DOI: https://doi.org/10.1007/978-1-4842-4113-4
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Valentina Porcu 2018
Softcover ISBN: 978-1-4842-4112-7Published: 20 December 2018
eBook ISBN: 978-1-4842-4113-4Published: 19 December 2018
Edition Number: 1
Number of Pages: XV, 260
Number of Illustrations: 80 b/w illustrations