Learn to use powerful Python libraries for effective data processing and analysis
About This Book
- Learn the basic processing steps in data analysis and how to use Python in this area through supported packages, especially Numpy, Pandas, and Matplotlib
- Create, manipulate, and analyze your data to extract useful information to optimize your system
- A hands-on guide to help you learn data analysis using Python
Who This Book Is For
If you are a Python developer who wants to get started with data analysis and you need a quick introductory guide to the python data analysis libraries, then this book is for you.
What You Will Learn
- Understand the importance of data analysis and get familiar with its processing steps
- Get acquainted with Numpy to use with arrays and array-oriented computing in data analysis
- Create effective visualizations to present your data using Matplotlib
- Process and analyze data using the time series capabilities of Pandas
- Interact with different kind of database systems, such as file, disk format, Mongo, and Redis
- Apply the supported Python package to data analysis applications through examples
- Explore predictive analytics and machine learning algorithms using Scikit-learn, a Python library
In Detail
Data analysis is the process of applying logical and analytical reasoning to study each component of data. Python is a multi-domain, high-level, programming language. It's often used as a scripting language because of its forgiving syntax and operability with a wide variety of different eco-systems. Python has powerful standard libraries or toolkits such as Pylearn2 and Hebel, which offers a fast, reliable, cross-platform environment for data analysis.
With this book, we will get you started with Python data analysis and show you what its advantages are.
The book starts by introducing the principles of data analysis and supported libraries, along with NumPy basics for statistic and data processing. Next it provides an overview of the Pandas package and uses its powerful features to solve data processing problems.
Moving on, the book takes you through a brief overview of the Matplotlib API and some common plotting functions for DataFrame such as plot. Next, it will teach you to manipulate the time and data structure, and load and store data in a file or database using Python packages. The book will also teach you how to apply powerful packages in Python to process raw data into pure and helpful data using examples.
Finally, the book gives you a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or build helpful products, such as recommendations and predictions using scikit-learn.
Style and approach
This is an easy-to-follow, step-by-step guide to get you familiar with data analysis and the libraries supported by Python. Topics are explained with real-world examples wherever required.
Phuong Vo.T.H has a MSc degree in computer science, which is related to machine learning. After graduation, she continued to work in some companies as a data scientist. She has experience in analyzing users' behavior and building recommendation systems based on users' web histories. She loves to read machine learning and mathematics algorithm books, as well as data analysis articles.
Martin Czygan studied German literature and computer science in Leipzig, Germany. He has been working as a software engineer for more than 10 years. For the past eight years, he has been diving into Python, and is still enjoying it. In recent years, he has been helping clients to build data processing pipelines and search and analytics systems. His consultancy can be found at http://www.xvfz.net.
