Book Description
Learn how to apply powerful data analysis techniques with popular open source Python modules
About This Book
 Find, manipulate, and analyze your data using the Python 3.5 libraries
 Perform advanced, highperformance linear algebra and mathematical calculations with clean and efficient Python code
 An easytofollow guide with realistic examples that are frequently used in realworld data analysis projects.
Who This Book Is For
This book is for programmers, scientists, and engineers who have the knowledge of Python and know the basics of data science. It is for those who wish to learn different data analysis methods using Python 3.5 and its libraries. This book contains all the basic ingredients you need to become an expert data analyst.
What You Will Learn
 Install open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikitlearn,theano, keras, and tensorflow on various platforms
 Prepare and clean your data, and use it for exploratory analysis
 Manipulate your data with Pandas
 Retrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5
 Visualize your data with open source libraries such as matplotlib, bokeh, and plotly
 Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian
 Understand signal processing and time series data analysis
 Get to grips with graph processing and social network analysis
In Detail
Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks.
With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis.
The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikitlearn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Style and approach
The book takes a very comprehensive approach to enhance your understanding of data analysis. Sufficient realworld examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your daytoday work. Packed with clear, easy to follow examples, this book will turn you into an ace data analyst in no time.
Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.
Publisher Resources
Table of Contents

Python Data Analysis  Second Edition
 Python Data Analysis  Second Edition
 Credits
 About the Author
 About the Reviewers
 www.PacktPub.com
 Customer Feedback
 Preface
 1. Getting Started with Python Libraries

2. NumPy Arrays
 The NumPy array object
 Creating a multidimensional array
 Selecting NumPy array elements
 NumPy numerical types
 Onedimensional slicing and indexing
 Manipulating array shapes
 Creating array views and copies
 Fancy indexing
 Indexing with a list of locations
 Indexing NumPy arrays with Booleans
 Broadcasting NumPy arrays
 Summary
 References

3. The Pandas Primer
 Installing and exploring Pandas
 The Pandas DataFrames
 The Pandas Series
 Querying data in Pandas
 Statistics with Pandas DataFrames
 Data aggregation with Pandas DataFrames
 Concatenating and appending DataFrames
 Joining DataFrames
 Handling missing values
 Dealing with dates
 Pivot tables
 Summary
 References
 4. Statistics and Linear Algebra

5. Retrieving, Processing, and Storing Data
 Writing CSV files with NumPy and Pandas
 The binary .npy and pickle formats
 Storing data with PyTables
 Reading and writing Pandas DataFrames to HDF5 stores
 Reading and writing to Excel with Pandas
 Using REST web services and JSON
 Reading and writing JSON with Pandas
 Parsing RSS and Atom feeds
 Parsing HTML with Beautiful Soup
 Summary
 Reference
 6. Data Visualization
 7. Signal Processing and Time Series
 8. Working with Databases
 9. Analyzing Textual Data and Social Media
 10. Predictive Analytics and Machine Learning
 11. Environments Outside the Python Ecosystem and Cloud Computing
 12. Performance Tuning, Profiling, and Concurrency
 A. Key Concepts
 B. Useful Functions
 C. Online Resources
Product Information
 Title: Python Data Analysis  Second Edition
 Author(s):
 Release date: March 2017
 Publisher(s): Packt Publishing
 ISBN: 9781787127487