Panda longs to play with Pandora next door, but he's too shy. Pandora longs to say "Hello, Panda!" but she's much too shy, too. Will these two shy pandas ever muster up the courage to speak to each other?

This book introduces two types of pandas and other Asian animals. The story shows how hard survival is for animals who can eat only one kind of food.

Describes the life and characteristics of the endangered giant panda and the vulnerable red panda. Includes information about their physical characteristics, habitats, diet, threats from predators, reproduction, and endangered species statuses.

Understanding the complex relationships between humans and the natural world is essential for achieving environmental sustainability and improving human well-being, yet many studies are unable to reveal complex interactions and hidden trends. This is the first book to synthesize the findings and approaches of long-term integrated research in a model coupled human and natural system, and to illustrate their applications to regional, national, and global scales. It features a classic long-term interdisciplinary research project in the Wolong Nature Reserve of China, which contains one of the largest wild populations of the world-famous endangered giant pandas. Bringing together a team of contributors from both the natural and social sciences, this book explores how a long-term interdisciplinary and model system approach is essential to uncover the common patterns and mechanisms of coupled systems, to develop ideas and methods for studying and managing other coupled systems, and ultimately to contribute to the development of theories about coupled systems for sustainability. Pandas and People will be essential reading for scholars interested in the interface of the natural and social sciences, including ecologists, conservation biologists, environmental scientists, sustainability scientists, wildlife biologists, forest scientists, sociologists, anthropologists, economists, and political scientists. It will also be a valuable reference for policy makers, natural resource managers, and graduate students.

Combines the latest findings from the field and the laboratory with panel and workshop summaries from a recent international conference.

A logo on products ranging from chopsticks and toilet paper to cell phones and automobiles, the panda is one of the most ubiquitous images in China and throughout the world. Yet the panda holds little notable historical significance in China. Although it has existed in the territory of present-day China since the Pliocene epoch, its widespread popularity there is not only recent, but almost sudden. In Panda Nation, E. Elena Songster links the emergence of the giant panda as a national symbol to the development of nature protection in the People's Republic of China. The panda's transformation into a national treasure exemplifies China's efforts in the mid-twentieth century to distinguish itself as a nation through government-directed science and popular nationalism. The story of the panda's iconic rise offers a striking reflection of China's recent and dramatic ascent as a nation in global status.

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms