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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.
Part I. Empirical and theoretical foundations -- Part II. Model coupled human and natural system -- Part III. Across local to global coupled human and natural systems -- Part IV. Perspectives
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.
The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning
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
An introduction to the habitat, physical characteristics, behavior, and life cycle of the giant panda, an endangered type of bear which lives in the mountain forests of southwestern China.