Pandas python data analysis von chen daniel (17 Ergebnisse)

Paperback. 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 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.…

Paperback. Zustand: 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 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.…

Paperback. Zustand: Good. No Jacket. Former library book; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Paperback. Zustand: Fair. No Jacket. Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less.

Paperback. Zustand: As New. No Jacket. Pages are clean and are not marred by notes or folds of any kind. ~ ThriftBooks: Read More, Spend Less.

Paperback. Zustand: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Zustand: Good. : Este libro es una introducción práctica y completa al análisis de datos con Python utilizando la biblioteca Pandas. Daniel Y. Chen presenta los conceptos clave a través de ejemplos sencillos pero prácticos, construyendo sobre ellos para resolver problemas del mundo real. Aprenderás a limpiar, estructurar y visualizar datos, así como a construir modelos para predicción, clustering e inferencia. También se ofrecen consejos sobre rendimiento y escalabilidad, y se introduce el ecosistema más amplio de análisis de datos de Python. EAN: 9780134546933 Tipo: Libros Categoría: Tecnología|Ciencias Título: Pandas for Everyone: Python Data Analysis Autor: Daniel Y. Chen Editorial: Addison-Wesley Professional Idioma: en Páginas: 416 Formato: tapa blanda.…

Paperback. Zustand: Good. Manage and Automate Data Analysis with Pandas 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 data sets.Pandas for Everyone, 2nd Edition, 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.New features to the second edition include:  Extended coverage of plotting and the seaborn data visualization libraryExpanded examples and resourcesUpdated Python 3.9 code and packages coverage, including statsmodels and scikit-learn librariesOnline bonus material on geopandas, Dask, and creating interactive graphics with Altair Chen 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.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 data sets and handle missing dataReshape, tidy, and clean data sets so they?re easier to work withConvert data types and manipulate text stringsApply functions to scale data manipulationsAggregate, transform, and filter large data sets 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? oneRegularize to overcome overfitting and improve performanceUse clustering in unsupervised machine learning. …

Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

Paperback. 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 dataCreate plots with matplotlib, seaborn, and pandasCombine datasets and handle missing dataReshape, tidy, and clean datasets so theyre 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 bestRegularize to overcome overfitting and improve performanceUse 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.…

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: New.

Paperback. Zustand: Brand New. 400 pages. 9.25x7.00x1.00 inches. In Stock.

Paperback. Zustand: Brand New. 2nd edition. 512 pages. 9.02x6.97x1.34 inches. In Stock.

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.…

Taschenbuch. Zustand: Neu. Neuware - Manage and Automate Data Analysis with Pandas in PythonToday, 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.Pandas for Everyone, 2nd Edition, 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.New features to the second edition include: - Extended coverage of plotting and the seaborn data visualization library- Expanded examples and resources- Updated Python 3.9 code and packages coverage, including statsmodels and scikit-learn libraries- Online bonus material on geopandas, Dask, and creating interactive graphics with AltairChen 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.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 data sets and handle missing data- Reshape, tidy, and clean data sets 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 data sets 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" one- Regularize to overcome overfitting and improve performance- Use clustering in unsupervised machine learning.…