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ISBN 10: 0137891156 ISBN 13: 9780137891153
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ISBN 10: 0134546938 ISBN 13: 9780134546933
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ISBN 10: 0134546938 ISBN 13: 9780134546933
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ISBN 10: 0134546938 ISBN 13: 9780134546933
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ISBN 10: 0134546938 ISBN 13: 9780134546933
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Verlag: Pearson Education (US), United States, Boston, 2018
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In den WarenkorbPaperback. Zustand: Very Good. 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 youre 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 theyre 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. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
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ISBN 10: 0134546938 ISBN 13: 9780134546933
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ISBN 10: 0137891156 ISBN 13: 9780137891153
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ISBN 10: 936159222X ISBN 13: 9789361592225
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Verlag: Pearson Education Dez 2017, 2017
ISBN 10: 0134546938 ISBN 13: 9780134546933
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Taschenbuch. Zustand: Neu. Neuware - 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 dataCreate plots with matplotlib, seaborn, and pandasCombine datasets and handle missing dataReshape, tidy, and clean datasets so they re easier to work withConvert data types and manipulate text stringsApply functions to scale data manipulationsAggregate, transform, and filter large datasets with groupbyLeverage Pandas advanced date and time capabilitiesFit linear models using statsmodels and scikit-learn librariesUse generalized linear modeling to fit models with different response variablesCompare multiple models to select the best Regularize to overcome overfitting and improve performanceUse clustering in unsupervised machine learning.