Pandas helps to alleviate a genuinely complex situation in data analytics libraries. Many incumbent languages aren't approachable or are fairly unproductive in general computing tasks in comparison to Python. However with Pandas it's easy to begin working with tabular datasets in a language that's easier to learn and use.
Instant Data Intensive Apps with pandas How-to starts with Pandas' functionalities such as joining datasets, cleaning data, and other data munging tasks. It quickly moves onto building a data reporting tool, which consists of analysis in Pandas to determine what's relevant and present that relevant data in an easy-to-consume manner.
Instant Data Intensive Apps with pandas How-to starts with data manipulation and other practical tasks for a fundamental understanding, and through successive recipes you will gain a more profitable understanding of Pandas.
Throughout this book the recipes are presented in a structured way. It starts with data transformation techniques, but builds up to more complex examples such as performing statistical analysis and integrating Pandas objects with web applications. The other recipes cover visualization and machine learning, among other things.
Instant Data Intensive Apps with pandas How-to will get the reader up and running quickly with Pandas and put the user in a position to move up the learning curve faster.
Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. This book has a practical approach with step-by-step recipes to help readers get to grips with Pandas.
Who this book is for
Users of other data analysis tools will find value in seeing tasks they commonly encounter translated to Pandas and users of Python will encounter an introduction to a very impressive tool in a syntax they inherently know. In terms of general skills, it is assumed that the reader understands basic data structures such as arrays or lists dictionaries or hash map as well as having some understanding of command line work. Installing Pandas is not covered, but the online documentation is straightforward. Also, readers are encouraged to use IPython to interact and experiment with the code.