Pandas In Action

Take the next steps in your data science career! This friendly and hands-on guide shows you how to start mastering Pandas with skills you already know from spreadsheet software. In Pandas in Action you will learn how to: Import datasets, identify issues with their data structures, and optimize them for efficiency Sort, filter, pivot, and draw conclusions from a dataset and its subsets Identify trends from text-based and time-based data Organize, group, merge, and join separate datasets Use a GroupBy object to store multiple DataFrames Pandas has rapidly become one of Python's most popular data analysis libraries. In Pandas in Action, a friendly and example-rich introduction, author Boris Paskhaver shows you how to master this versatile tool and take the next steps in your data science career. You’ll learn how easy Pandas makes it to efficiently sort, analyze, filter and munge almost any type of data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Data analysis with Python doesn’t have to be hard. If you can use a spreadsheet, you can learn pandas! While its grid-style layouts may remind you of Excel, pandas is far more flexible and powerful. This Python library quickly performs operations on millions of rows, and it interfaces easily with other tools in the Python data ecosystem. It’s a perfect way to up your data game. About the book Pandas in Action introduces Python-based data analysis using the amazing pandas library. You’ll learn to automate repetitive operations and gain deeper insights into your data that would be impractical—or impossible—in Excel. Each chapter is a self-contained tutorial. Realistic downloadable datasets help you learn from the kind of messy data you’ll find in the real world. What's inside Organize, group, merge, split, and join datasets Find trends in text-based and time-based data Sort, filter, pivot, optimize, and draw conclusions Apply aggregate operations About the reader For readers experienced with spreadsheets and basic Python programming. About the author Boris Paskhaver is a software engineer, Agile consultant, and online educator. His programming courses have been taken by 300,000 students across 190 countries. Table of Contents PART 1 CORE PANDAS 1 Introducing pandas 2 The Series object 3 Series methods 4 The DataFrame object 5 Filtering a DataFrame PART 2 APPLIED PANDAS 6 Working with text data 7 MultiIndex DataFrames 8 Reshaping and pivoting 9 The GroupBy object 10 Merging, joining, and concatenating 11 Working with dates and times 12 Imports and exports 13 Configuring pandas 14 Visualization

When ten-year-old Journey Song hears that two pandas are being held in a warehouse in her neighborhood, she worries that they may be hungry, cold and lonely. Horrified to learn that the pandas, originally destined for a zoo in Washington, might be shipped back to China because of a diplomatic spat between China and the United States, Journey rallies her friends and neighbors on the poverty-stricken Eastside. Her infectious enthusiasm for all things panda is hard to resist, and soon she's getting assistance from every corner of her tight-knit neighborhood.

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Movable features depict the life cycle, physical characteristics, and behavior of pandas in the wild.

DESCRIPTION Practice makes perfect pandas. Work out your pandas skills against dozens of real-world challenges, each carefully designed to build an intuitive knowledge of essential pandas tasks. In Pandas Workout, discover 50 exercises that will strengthen your pandas skills to a level of automatic fluency. You'll test yourself against common pandas challenges such as data cleaning, and explore real-world datasets such as New York Taxis, Kickstarter projects, and global tourist spending. Detailed explanations help guide your success and make your new skills stick. You'll even get a big boost to productivity, with tasks that used to mean a trip to StackOverflow now a natural part of your skillset.

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

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.