{"product_id":"pandas-for-everyone-python-data-analysis-9780137891153","title":"Pandas for Everyone: Python Data Analysis","description":"\u003cp\u003e\u003cstrong\u003eManage and Automate Data Analysis with Pandas in Python\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eToday, 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 data sets. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cstrong\u003e\u003cem\u003ePandas for Everyone, 2nd Edition, \u003c\/em\u003e\u003c\/strong\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 data science problems such as using regularization to prevent data overfitting, or when to use unsupervised machine learning methods to find the underlying structure in a data set. \u003cp\u003e\u003c\/p\u003eNew features to the second edition include:  \u003cul\u003e \u003cli\u003eExtended coverage of plotting and the seaborn data visualization library\u003c\/li\u003e \u003cli\u003eExpanded examples and resources\u003c\/li\u003e \u003cli\u003eUpdated Python 3.9 code and packages coverage, including statsmodels and scikit-learn libraries\u003c\/li\u003e \u003cli\u003eOnline bonus material on geopandas, Dask, and creating interactive graphics with Altair\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cbr\u003eChen gives you a jumpstart on using Pandas with a realistic data set and covers combining data sets, handling missing data, and structuring data sets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eOnce 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 data sets and handle missing data\u003c\/li\u003e \u003cli\u003eReshape, tidy, and clean data sets 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 data sets 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\" one\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\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003eDaniel Chen\u003c\/strong\u003e is a graduate student in the Interdisciplinary PhD program in Genetics, Bioinformatics \u0026amp; Computational Biology (GBCB) at Virginia Polytechnic Institute and State University (Virginia Tech). He is involved with Software Carpentry as an instructor, Mentoring Committee Member, and currently serves as the Assessment Committee Chair. He completed his Masters in Public Health at Columbia University Mailman School of Public Health in Epidemiology with a certificate in Advanced Epidemiology and currently extending his Master's thesis work in the Social and Decision Analytics Laboratory under the Virginia Bioinformatics Institute on attitude diffusion in social networks.\u003c\/p\u003e\u003cbr\u003e","brand":"Addison-Wesley Professional","offers":[{"title":"Default Title","offer_id":50497451229458,"sku":"9780137891153","price":43.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_b40196ff-8d88-4550-940f-37c0c60202ce.jpg?v=1730713334","url":"https:\/\/surprise-castle.myshopify.com\/products\/pandas-for-everyone-python-data-analysis-9780137891153","provider":"Surprise Castle","version":"1.0","type":"link"}