{"product_id":"pandas-for-everyone-python-data-analysis-9780134546933","title":"Pandas for Everyone: Python Data Analysis","description":"\u003cb\u003eThe Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python\u003c\/b\u003e  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.  \u003ci\u003e\u003cb\u003ePandas for Everyone\u003c\/b\u003e\u003c\/i\u003e 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.  \u003cul\u003e \u003cli\u003eWork with DataFrames and Series, and import or export data\u003c\/li\u003e \u003cli\u003eCreate plots with matplotlib, seaborn, and pandas\u003c\/li\u003e \u003cli\u003eCombine datasets and handle missing data\u003c\/li\u003e \u003cli\u003eReshape, tidy, and clean datasets so they're easier to work with\u003c\/li\u003e \u003cli\u003eConvert data types and manipulate text strings\u003c\/li\u003e \u003cli\u003eApply functions to scale data manipulations\u003c\/li\u003e \u003cli\u003eAggregate, transform, and filter large datasets with groupby\u003c\/li\u003e \u003cli\u003eLeverage Pandas' advanced date and time capabilities\u003c\/li\u003e \u003cli\u003eFit linear models using statsmodels and scikit-learn libraries\u003c\/li\u003e \u003cli\u003eUse generalized linear modeling to fit models with different response variables\u003c\/li\u003e \u003cli\u003eCompare multiple models to select the \"best\"\u003c\/li\u003e \u003cli\u003eRegularize to overcome overfitting and improve performance\u003c\/li\u003e \u003cli\u003eUse clustering in unsupervised machine learning\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eDaniel Chen\u003c\/b\u003e is a graduate student in the interdisciplinary PhD program in Genetics, Bioinformatics \u0026amp; Computational Biology (GBCB) at Virginia Tech. He is involved with Software Carpentry as an instructor and lesson maintainer. He completed his master's degree in public health at Columbia University Mailman School of Public Health in Epidemiology, and currently works at the Social and Decision Analytics Laboratory under the Biocomplexity Institute of Virginia Tech where he is working with data to inform policy decision-making. He is the author of \u003ci\u003ePandas for Everyone\u003c\/i\u003e and \u003ci\u003ePandas Data Analysis with Python Fundamentals LiveLessons.\u003c\/i\u003e\u003cbr\u003e","brand":"Addison-Wesley Professional","offers":[{"title":"Default Title","offer_id":50318100889874,"sku":"9780134546933","price":36.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_873c95dc-c2e0-4e99-82c5-fc7ca1359a37.jpg?v=1727548135","url":"https:\/\/surprise-castle.myshopify.com\/products\/pandas-for-everyone-python-data-analysis-9780134546933","provider":"Surprise Castle","version":"1.0","type":"link"}