This book is a practical guide for getting started with Python in data analysis, including exercises and a case study on data formatting. It covers topics such as data preparation, meaning extraction, and visualization using iPython. It is designed for beginners with some Python experience and prior data analysis or data science knowledge.

Format: Paperback / softback
Length: 97 pages
Publication date: 23 February 2018
Publisher: APress

This comprehensive guide is designed to help you get started with Python in data analysis. It includes three practical exercises and a case study on effectively importing and exporting data in Python code. Whether you are a novice or have some experience with Python, this book will assist you in unlocking the power of data analysis.

Learn Data Analysis with Python offers a practical approach to data analysis, making it accessible to beginners and experienced Python users alike. The book begins with an introduction to Python and its data analysis capabilities, providing a solid foundation for those new to the language.

Chapter 1: Getting Started with Python
In this chapter, you will learn the basics of Python, including its syntax, data types, and control structures. You will also explore the iPython environment, which is a powerful interactive shell for Python that offers a wide range of features for data analysis and visualization.

Chapter 2: Importing and Exporting Data
In this chapter, you will learn how to import data from various sources, such as CSV files, Excel sheets, and databases. You will also learn how to export data in different formats, such as CSV, Excel, and JSON, to facilitate data sharing and collaboration.

Chapter 3: Data Preparation and Cleaning
Data preparation and cleaning are critical steps in data analysis. In this chapter, you will learn how to preprocess data, remove missing values, handle outliers, and normalize data. You will also explore the use of libraries such as Pandas and NumPy for data manipulation and analysis.

Chapter 4: Finding Meaning in Data
Data analysis is not just about crunching numbers; it is also about finding patterns and insights that can help you make informed decisions. In this chapter, you will learn how to use statistical analysis techniques, such as regression analysis, hypothesis testing, and cluster analysis, to uncover meaningful patterns in data.

Chapter 5: Visualizing Data
Visualization is an essential tool for data analysis and interpretation. In this chapter, you will learn how to create interactive visualizations using libraries such

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.

Weight: 200g
Dimension: 234 x 162 x 7 (mm)
ISBN-13: 9781484234853
Edition number: 1st ed.