Overview
- A quick and practical hands-on guide to learning and using Python in data analysis
- Includes three exercises and one analysis project case study
- Learn to visualize data using Python
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Table of contents (6 chapters)
Keywords
About this book
Each lesson is, as much as possible, self-contained to allow you to dip in and out of the examples as your needs dictate. If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python. You can then take these techniques and apply them directly to your own projects.
If you aren’t using Python for data analysis, this book takes you through the basics at the beginning to give you a solid foundation in the topic. As you work your way through the book you will have a better of idea of how to use Python for data analysis when you are finished.
What You Will Learn
- Get data into and out of Python code
- Prepare the data and its format
- Find the meaning of the data
- Visualize the data using iPython
Who This Book Is For
Those who want to learn data analysis using Python. Some experience with Python is recommended but not required, as is some prior experience with data analysis or data science.
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Authors and Affiliations
About the authors
Dave Wolf is a certified Project Management Professional (PMP) with over twenty years' experience as a software developer, analyst and trainer. His latest projects include collaboratively developing training materials and programming bootcamps for Java and Python.
Bibliographic Information
Book Title: Learn Data Analysis with Python
Book Subtitle: Lessons in Coding
Authors: A.J. Henley, Dave Wolf
DOI: https://doi.org/10.1007/978-1-4842-3486-0
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: A.J. Henley and Dave Wolf 2018
Softcover ISBN: 978-1-4842-3485-3Published: 23 February 2018
eBook ISBN: 978-1-4842-3486-0Published: 22 February 2018
Edition Number: 1
Number of Pages: IX, 97
Number of Illustrations: 15 illustrations in colour
Topics: Python, Data Mining and Knowledge Discovery, Big Data, Big Data/Analytics