- Python Data Science Cookbook
- Table of Contents
- Python Data Science Cookbook
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Preface
- 1. Python for Data Science
- Introduction
- Using dictionary objects
- Working with a dictionary of dictionaries
- Working with tuples
- Using sets
- Writing a list
- Creating a list from another list - list comprehension
- Using iterators
- Generating an iterator and a generator
- Using iterables
- Passing a function as a variable
- Embedding functions in another function
- Passing a function as a parameter
- Returning a function
- Altering the function behavior with decorators
- Creating anonymous functions with lambda
- Using the map function
- Working with filters
- Using zip and izip
- Processing arrays from the tabular data
- Preprocessing the columns
- Sorting lists
- Sorting with a key
- Working with itertools
- 2. Python Environments
- 3. Data Analysis – Explore and Wrangle
- Introduction
- Analyzing univariate data graphically
- Grouping the data and using dot plots
- Using scatter plots for multivariate data
- Using heat maps
- Performing summary statistics and plots
- Using a box-and-whisker plot
- Imputing the data
- Performing random sampling
- Scaling the data
- Standardizing the data
- Performing tokenization
- Removing stop words
- Stemming the words
- Performing word lemmatization
- Representing the text as a bag of words
- Calculating term frequencies and inverse document frequencies
- 4. Data Analysis – Deep Dive
- 5. Data Mining – Needle in a Haystack
- 6. Machine Learning 1
- 7. Machine Learning 2
- 8. Ensemble Methods
- 9. Growing Trees
- 10. Large-Scale Machine Learning – Online Learning
- Index
This book is intended for all levels of data science professionals, both students and practitioners from novice to experts. Different recipes in the chapters cater to the needs of different audiences. Novice readers can spend some time in getting themselves acquainted with data science in the first five chapters. Experts can refer to the later chapters to refer/understand how advanced techniques are implemented using Python. The book covers just enough mathematics and provides the necessary references for computer programmers who wish to understand data science. People from a non-Python background can effectively use this book. The first chapter of the book introduces Python as a programming language for data science. It will be helpful if you have some prior basic programming experience. The book is mostly self-contained and introduces data science to a new reader and can help him become an expert in this trade.
-
No Comment