Book Description
Learn how to apply powerful data analysis techniques with popular open source Python modules
In Detail
Python is a multiparadigm programming language well suited for both objectoriented application development as well as functional design patterns. Python has become the language of choice for data scientists for data analysis, visualization, and machine learning. It will give you velocity and promote high productivity.
This book will teach novices about data analysis with Python in the broadest sense possible, covering everything from data retrieval, cleaning, manipulation, visualization, and storage to complex analysis and modeling. It focuses on a plethora of open source Python modules such as NumPy, SciPy, matplotlib, pandas, IPython, Cython, scikitlearn, and NLTK. In later chapters, the book covers topics such as data visualization, signal processing, and timeseries analysis, databases, predictive analytics and machine learning. This book will turn you into an ace data analyst in no time.
What You Will Learn
 Install open source Python modules on various platforms
 Get to know about the fundamentals of NumPy including arrays
 Manipulate data with pandas
 Retrieve, process, store, and visualize data
 Understand signal processing and timeseries data analysis
 Work with relational and NoSQL databases
 Discover more about data modeling and machine learning
 Get to grips with interoperability and cloud computing
Publisher Resources
Table of Contents

Python Data Analysis
 Table of Contents
 Python Data Analysis
 Credits
 About the Author
 About the Reviewers
 www.PacktPub.com
 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
 3. Statistics and Linear Algebra

4. pandas Primer
 Installing and exploring pandas
 pandas DataFrames
 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
 Remote data access
 Summary

5. Retrieving, Processing, and Storing Data
 Writing CSV files with NumPy and pandas
 Comparing the NumPy .npy binary format and pickling pandas DataFrames
 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
 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
 Index
Product Information
 Title: Python Data Analysis
 Author(s):
 Release date: October 2014
 Publisher(s): Packt Publishing
 ISBN: 9781783553358